#Tarea 08 y 09 #Mineria de Datos I #Ricardo Zamora Mennigke

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library(dplyr)
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Pregunta 1: Suponga que trabajamos para un banco y se nos pide predecir el monto promedio de deuda en tarjeta de cr´edito de una cartera de clientes relativamente nuevos, basado en otra cartera de comportamiento y estructura similar de la cual s´ı se tiene informaci´on de deuda en tarjeta de cr´edito. En este ejercicio hacemos uso de la tabla de datos DeudaCredito.csv que contiene informaci´on de los clientes en una de las principales carteras de cr´edito del banco, e incluye variables que describen cada cliente tanto dentro del banco como fuera de ´este. Esta tabla de datos contiene 400 clientes y 11 variables que los describen. Seguidamente se explican las variables que conforman la tabla.

setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase8")
datos<-read.csv("DeudaCredito.csv",dec='.',header=T)
str(datos)
## 'data.frame':    400 obs. of  12 variables:
##  $ X          : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Ingreso    : num  14.9 106 104.6 148.9 55.9 ...
##  $ Limite     : int  3606 6645 7075 9504 4897 8047 3388 7114 3300 6819 ...
##  $ CalifCredit: int  283 483 514 681 357 569 259 512 266 491 ...
##  $ Tarjetas   : int  2 3 4 3 2 4 2 2 5 3 ...
##  $ Edad       : int  34 82 71 36 68 77 37 87 66 41 ...
##  $ Educacion  : int  11 15 11 11 16 10 12 9 13 19 ...
##  $ Genero     : Factor w/ 2 levels "Femenino","Masculino": 2 1 2 1 2 2 1 2 1 1 ...
##  $ Estudiante : Factor w/ 2 levels "No","Si": 1 2 1 1 1 1 1 1 1 2 ...
##  $ Casado     : int  1 1 0 0 1 0 0 0 0 1 ...
##  $ Etnicidad  : Factor w/ 3 levels "Afrodescendiente",..: 3 2 2 2 3 3 1 2 3 1 ...
##  $ Balance    : int  333 903 580 964 331 1151 203 872 279 1350 ...
suppressMessages(suppressWarnings(library(FactoMineR)))
suppressMessages(suppressWarnings(library(car)))
Atipicos<-(Boxplot(~Balance, data=datos, id.method="y",col="Blue")) #Monto promedio de deuda en tarjeta de cr´edito del cliente, en d´olares

Atipicos<-(Boxplot(~Ingreso, data=datos, id.method="y",col="Blue")) #Ingreso: Ingreso del cliente, en miles de d´olares.

Atipicos<-(Boxplot(~CalifCredit, data=datos, id.method="y",col="Blue")) #Ingreso: Ingreso del cliente, en miles de d´olares.

# Elimino variables categóricas
datos2 <- datos[,-c(1,8,9,11)] ##verificar correlaciones con variables numericas
head(datos2)
##   Ingreso Limite CalifCredit Tarjetas Edad Educacion Casado Balance
## 1  14.891   3606         283        2   34        11      1     333
## 2 106.025   6645         483        3   82        15      1     903
## 3 104.593   7075         514        4   71        11      0     580
## 4 148.924   9504         681        3   36        11      0     964
## 5  55.882   4897         357        2   68        16      1     331
## 6  80.180   8047         569        4   77        10      0    1151
library(corrplot)
## corrplot 0.84 loaded
matriz.correlacion<-cor(datos2)
corrplot(matriz.correlacion)

Debe tenerse en cuenta que aqui se denota unicamente un punto de vista inicial al eliminar variables categoricas y respuesta para analizar si existe cierta correlacion entre variables, sirve como un punto de vista inicial, para ver si pueden existir ciertos problemas de estimacion. Se puede observar que las variables predictoras Ingreso, Limite y Calificacion, estan significativamente correlacionadas con la variable de respuesta, es decir, en otros casos podrian agruparse como una sola variable. Esto es un indicativo de que al estimar las variables puedan comprometer la estimacion por lo que se debe tomar al analizar la respuesta y el resultado final del modelo.

muestra <- sample(1:nrow(datos),floor(nrow(datos)*0.20))
ttesting <- datos[muestra,]
taprendizaje <- datos[-muestra,]
nrow(ttesting)
## [1] 80
nrow(taprendizaje)
## [1] 320
  1. Basado en las estad´ısticas b´asicas explique cu´al variable num´erica parece ser la mejor para predecir la deuda en tarjeta de cr´etido.
summary(datos)
##        X            Ingreso           Limite       CalifCredit   
##  Min.   :  1.0   Min.   : 10.35   Min.   :  855   Min.   : 93.0  
##  1st Qu.:100.8   1st Qu.: 21.01   1st Qu.: 3088   1st Qu.:247.2  
##  Median :200.5   Median : 33.12   Median : 4622   Median :344.0  
##  Mean   :200.5   Mean   : 45.22   Mean   : 4736   Mean   :354.9  
##  3rd Qu.:300.2   3rd Qu.: 57.47   3rd Qu.: 5873   3rd Qu.:437.2  
##  Max.   :400.0   Max.   :186.63   Max.   :13913   Max.   :982.0  
##     Tarjetas          Edad         Educacion           Genero    Estudiante
##  Min.   :1.000   Min.   :23.00   Min.   : 5.00   Femenino :207   No:360    
##  1st Qu.:2.000   1st Qu.:41.75   1st Qu.:11.00   Masculino:193   Si: 40    
##  Median :3.000   Median :56.00   Median :14.00                             
##  Mean   :2.958   Mean   :55.67   Mean   :13.45                             
##  3rd Qu.:4.000   3rd Qu.:70.00   3rd Qu.:16.00                             
##  Max.   :9.000   Max.   :98.00   Max.   :20.00                             
##      Casado                  Etnicidad      Balance       
##  Min.   :0.0000   Afrodescendiente: 99   Min.   :   0.00  
##  1st Qu.:0.0000   Asiatico        :102   1st Qu.:  68.75  
##  Median :1.0000   Caucasico       :199   Median : 459.50  
##  Mean   :0.6125                          Mean   : 520.01  
##  3rd Qu.:1.0000                          3rd Qu.: 863.00  
##  Max.   :1.0000                          Max.   :1999.00

La mejor variable numerica para predecir la deuda en la tarjeta de credito es justamente la variable balance, ya que indica el Monto promedio de deuda en tarjeta de cr´edito del cliente, en d´olares. Dependiendo del estudio podria tambien ser interesante analizar la generacion de la calificacion crediticia pero esta es una calificacion que no

  1. Genere un modelo de regresi´on lineal m´ultiple incluyendo las todas las variables predictoras. ¿Cu´ales coeficientes obtiene para los β? D´e una interpretaci´on de 3 de los coeficientes que se obtienen en el modelo. ¿Cu´al variable parece tener m´as impacto sobre la variable a predecir y por qu´e?
modelo <- lm(Balance ~ ., data = datos)
modelo 
## 
## Call:
## lm(formula = Balance ~ ., data = datos)
## 
## Coefficients:
##        (Intercept)                   X             Ingreso              Limite  
##         -496.62039             0.04105            -7.80740             0.19052  
##        CalifCredit            Tarjetas                Edad           Educacion  
##            1.14249            17.83639            -0.62955            -1.09831  
##    GeneroMasculino        EstudianteSi              Casado   EtnicidadAsiatico  
##            9.54615           426.16715            -8.78055            16.85752  
## EtnicidadCaucasico  
##            9.29289
summary(modelo)
## 
## Call:
## lm(formula = Balance ~ ., data = datos)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -166.48  -77.62  -14.37   56.21  316.52 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        -496.62039   36.51325 -13.601  < 2e-16 ***
## X                     0.04105    0.04343   0.945   0.3452    
## Ingreso              -7.80740    0.23431 -33.321  < 2e-16 ***
## Limite                0.19052    0.03279   5.811  1.3e-08 ***
## CalifCredit           1.14249    0.49100   2.327   0.0205 *  
## Tarjetas             17.83639    4.34324   4.107  4.9e-05 ***
## Edad                 -0.62955    0.29449  -2.138   0.0332 *  
## Educacion            -1.09831    1.59817  -0.687   0.4924    
## GeneroMasculino       9.54615    9.98431   0.956   0.3396    
## EstudianteSi        426.16715   16.73077  25.472  < 2e-16 ***
## Casado               -8.78055   10.36758  -0.847   0.3976    
## EtnicidadAsiatico    16.85752   14.12112   1.194   0.2333    
## EtnicidadCaucasico    9.29289   12.24194   0.759   0.4483    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 98.8 on 387 degrees of freedom
## Multiple R-squared:  0.9552, Adjusted R-squared:  0.9538 
## F-statistic: 687.7 on 12 and 387 DF,  p-value: < 2.2e-16

Usando todos los datos se pueden sacar conclusiones al modelo mostrado abajo con solo training. En este caso los valores de β son los estimadores (estimate) del modelo. Aqui debe tenerse en cuenta que el modelo sin reducir seria: y = -496.62039 + 0.04105X - 7.80740 * Ingreso + … + 9.29289 * EtnicidadCaucasico Tomando como principal criterio las probabilidades existen varias variables que resultan significativas para la variable de respuesta especialmente el ingreso y si es Estdudiante. Es curioso ya que el t valor muestra un valor negativo igual que el estimador lo que indica que entre mayor ingreso menor problema crediticio. Contrario si es EstudianteSI esta variable tiene el mayor impacto, ya que si es estudiante su deuda aumenta segun el estimador 426.16715. Interpretando 3 coeficientes como ejemplo, por cada aumento en una unidad de Ingreso (en miles de dolares) la deuda en tarjeta de credito de la persona se reduce en 7,80740 dolares. Por cada tarjeta de credito adicional (Tarjetas) se estima que la persona tiene 17,83639 dolares mas un su monto de deuda en su tarjeta de credito. Finalmente por cada ano adicional en la edad del cliente se estima que su deuda es -0.62955 dolares mas baja.

# Calcula el modelo usando solo los datos de training
modelo.lm <- lm(Balance~., data = taprendizaje)
modelo.lm
## 
## Call:
## lm(formula = Balance ~ ., data = taprendizaje)
## 
## Coefficients:
##        (Intercept)                   X             Ingreso              Limite  
##          -503.0591              0.0123             -7.9026              0.2009  
##        CalifCredit            Tarjetas                Edad           Educacion  
##             1.0252             17.4851             -0.6203             -0.9079  
##    GeneroMasculino        EstudianteSi              Casado   EtnicidadAsiatico  
##            11.7868            442.5138             -7.7921             21.3282  
## EtnicidadCaucasico  
##            11.3099
  1. ¿Qu´e error se obtiene sobre la tabla de testing para el modelo de regresi´on lineal? Interprete las medidas de error obtenidas.
# Residual Sum of Square (RSS)
RSS <- function(Pred,Real) {
  ss <- sum((Real-Pred)^2)
  return(ss)
}

# NumPred es el número total de predictores por eso se resta 1 (que es realidad sumar 1)
RSE<-function(Pred,Real,NumPred) {
  N<-length(Real)-NumPred-1  # <- length(Real)-(NumPred+1)
  ss<-sqrt((1/N)*RSS(Pred,Real))
  return(ss)
}

MSE <- function(Pred,Real) {
  N<-length(Real)
  ss<-(1/N)*RSS(Pred,Real)
  return(ss)
}

error.relativo <- function(Pred,Real) {
  ss<-sum(abs(Real-Pred))/sum(abs(Real))
  return(ss)
}

# Funciones para desplegar precisión
indices.precision <- function(real, prediccion,cantidad.variables.predictoras) {
  return(list(error.cuadratico = MSE(prediccion,real),
              raiz.error.cuadratico = RSE(prediccion,real,cantidad.variables.predictoras),
              error.relativo = error.relativo(prediccion,real),
              correlacion = as.numeric(cor(prediccion,real))))
}


# Gráfico de dispersión entre el valor real de la variable a predecir y la predicción del modelo.
plot.real.prediccion <- function(real, prediccion, modelo = "") {
  g <- ggplot(data = data.frame(Real = real, Prediccion = as.numeric(prediccion)), mapping = aes(x = Real, y = Prediccion)) +
    geom_point(size = 1, col = "dodgerblue3") +
    labs(title = paste0("Real vs Predicción", ifelse(modelo == "", "", paste(", con", modelo))),
         x = "Real",
         y = "Predicción")
  return(g)
}
numero.predictoras <- dim(datos)[2] - 1
# Hace la Predicción
prediccion <- predict(modelo.lm, ttesting)
# Medición de precisión
pre.lm <- indices.precision(ttesting$Balance, prediccion,numero.predictoras)
pre.lm
## $error.cuadratico
## [1] 11379.65
## 
## $raiz.error.cuadratico
## [1] 115.7057
## 
## $error.relativo
## [1] 0.1717528
## 
## $correlacion
## [1] 0.9707004

Medida de error en este caso se mide facilmente por la raiz del error cuadratico medio en terminos podria decirse para este caso mas “netos”. Ya que lo que indica este coeficiente es que la estimacion del modelo tiene un error absoluto estimado de + (mas) o (-) 94.58605 dolares en la estimacion final del Balance de la deuda en tarjetas de credito. El error relativo indica que el error de estimacion es aproximado de 12,68% y ademas la correlacion entre el valor real y el valor predicho es 0.9842869, es decir entre mas cercano a 1 mejor. En terminos generales la estimacion pues no es ideal pero tiene una estimacion predicha bastante significativamente precisa. Viendo de hecho la grafica Real vs Prediccion se denota que el modelo se ajusta bien a la funcion identidad, dando en realidad problemas en las estimaciones de clientes con deudas bajas o nulas, relacionado al residual standard error en 98,8.

# Gráfico real vs predicción, con curva de mejor ajuste lineal
g <- plot.real.prediccion(ttesting$Balance, prediccion, modelo = "Regresión Lineal")
g + geom_smooth(method = lm, size = 0.4, color = "red", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

prediccion <- predict(modelo,datos, interval="confidence")
hist(prediccion, col = "green")

  1. Si tuviera que eliminar alguna o algunas de las variables con la esperanza de que mejore la predicci´on ¿Cu´al o cu´ales de las variables eliminar´ıa? ¿El nuevo modelo mejora la predicci´on?

Eliminaria del modelo X, Educacion, Genero, casado, etnicidad. solo dejaria Ingreso, Limite, CalifCredit, Tarjetas, Edad y Estudiante, ya que mostrar significancia para predecir la variable en estudio.

modelo <- lm(Balance ~ Ingreso + Limite  + CalifCredit + Tarjetas + Edad + Estudiante, data = datos)
modelo
## 
## Call:
## lm(formula = Balance ~ Ingreso + Limite + CalifCredit + Tarjetas + 
##     Edad + Estudiante, data = datos)
## 
## Coefficients:
##  (Intercept)       Ingreso        Limite   CalifCredit      Tarjetas  
##    -493.7342       -7.7951        0.1937        1.0912       18.2119  
##         Edad  EstudianteSi  
##      -0.6241      425.6099
modelo <- lm(Balance ~ Ingreso + Limite  + CalifCredit + Tarjetas + Edad + Estudiante, data = datos)

numero.predictoras <- dim(datos)[2] - 1
# Hace la Predicción
prediccion <- predict(modelo, ttesting)
# Medición de precisión
pre.lm <- indices.precision(ttesting$Balance, prediccion,numero.predictoras)
pre.lm
## $error.cuadratico
## [1] 11005.44
## 
## $raiz.error.cuadratico
## [1] 113.7874
## 
## $error.relativo
## [1] 0.1675667
## 
## $correlacion
## [1] 0.9713879

La estimacion del modelo reducido tiene un error absoluto estimado de + (mas) o (-) 87.20528 dolares, inferior 94.58605 dolares en la estimacion final del Balance de la deuda en tarjetas de credito en el modelo completo, esto se explica porque al incluirse variables solo significativas el modelo no genera overfitting y permite eliminar variables que no aportan a la estimacion. El error relativo indica que el error de estimacion es aproximado de 11,48%, respecto al 12,68% y ademas la correlacion entre el valor real y el valor predicho es 0.9859838, respecto a 0.9842869, es decir, el modelo reducido se desmpena mejor.

# Gráfico real vs predicción, con curva de mejor ajuste lineal
g <- plot.real.prediccion(ttesting$Balance, prediccion, modelo = "Regresión Lineal")
g + geom_smooth(method = lm, size = 0.4, color = "red", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

Pregunta 2: Un cliente nos contrata para estudiar una posible oportunidad de negocio, y para ver si le es rentable quiere una predicci´on de las ventas potenciales de asientos de ni˜nos para autos en su tienda. Para ello hacemos uso de los datos AsientosNinno.csv los cual contienen detalles de ventas de asientos de ni˜nos para auto en una serie de tiendas similares a las del cliente, y adem´as los datos incluyen variables que definen caracter´ısticas de la tienda y su localidad. La tabla de datos est´a formada por 400 filas y 11 columnas. Seguidamente se explican las variables que conforman la tabla.

  1. Cargue la tabla de datos en R. En caso de ser necesario, recodificar las variables de forma adecuada. Seleccione la variable a predecir, y para medir el error tome un 15 % de la tabla de datos.
setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase8")
datos<-read.csv("AsientosNinno.csv",sep=';',dec='.',header=T)
str(datos)
## 'data.frame':    400 obs. of  12 variables:
##  $ X            : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Ventas       : num  9.5 11.22 10.06 7.4 4.15 ...
##  $ PrecioCompt  : int  138 111 113 117 141 124 115 136 132 132 ...
##  $ Ingreso      : int  73 48 35 100 64 113 105 81 110 113 ...
##  $ Publicidad   : int  11 16 10 4 3 13 0 15 0 0 ...
##  $ Poblacion    : int  276 260 269 466 340 501 45 425 108 131 ...
##  $ Precio       : int  120 83 80 97 128 72 108 120 124 124 ...
##  $ CalidadEstant: Factor w/ 3 levels "Bueno","Malo",..: 2 1 3 3 2 2 3 1 3 3 ...
##  $ Edad         : int  42 65 59 55 38 78 71 67 76 76 ...
##  $ Educacion    : int  17 10 12 14 13 16 15 10 10 17 ...
##  $ Urbano       : int  1 1 1 1 1 0 1 1 0 0 ...
##  $ USA          : int  1 1 1 1 0 1 0 1 0 1 ...
suppressMessages(suppressWarnings(library(FactoMineR)))
suppressMessages(suppressWarnings(library(car)))
Atipicos<-(Boxplot(~Ventas, data=datos, id.method="y",col="Blue")) #Monto promedio de deuda en tarjeta de cr´edito del cliente, en d´olares

# Elimino variables categóricas
datos2 <- datos[,-c(1,8)] ##verificar correlaciones con variables numericas
head(datos2)
##   Ventas PrecioCompt Ingreso Publicidad Poblacion Precio Edad Educacion Urbano
## 1   9.50         138      73         11       276    120   42        17      1
## 2  11.22         111      48         16       260     83   65        10      1
## 3  10.06         113      35         10       269     80   59        12      1
## 4   7.40         117     100          4       466     97   55        14      1
## 5   4.15         141      64          3       340    128   38        13      1
## 6  10.81         124     113         13       501     72   78        16      0
##   USA
## 1   1
## 2   1
## 3   1
## 4   1
## 5   0
## 6   1
library(corrplot)
matriz.correlacion<-cor(datos2)
corrplot(matriz.correlacion)

No hay mucha correlacion entre variables numericas predictoras.

muestra <- sample(1:nrow(datos),floor(nrow(datos)*0.15))
ttesting <- datos[muestra,]
taprendizaje <- datos[-muestra,]
nrow(ttesting)
## [1] 60
nrow(taprendizaje)
## [1] 340
  1. Aplique los modelos de regresi´on lineal m´ultiple, regresi´on Ridge y regresi´on Lasso incluyendo todas las variables predictoras. ¿Se anulan coeficientes para el caso de regresi´on Lasso?

En el caso de LASSO como se observara en la estimacion del punto 3, se eliminan PrecioCompt, Ingreso, Publicidad, Poblacion, CalidadEstantMedio, Edad, Educacion, Urbano y USA. Generan estimadores que aproximan a cero, dada la poca cantidad de variables que quedan, es bastante presumible que el caso ideal de Lasso optimo termine asemejando a una regresion clasica

  1. ¿Qu´e error se obtiene sobre la tabla de training para los 3 modelos generados anteriormente? ¿Cu´al considera que es el mejor seg´un la comparaci´on anterior?

Se aplican los modelos regresion lineal multiple, ridge y lasso, se generan varias simulaciones adicionales para mostrar un poco mas el comportamiento del lambda.

#Multiple

modelo <- lm(Ventas ~ ., data = datos)
modelo 
## 
## Call:
## lm(formula = Ventas ~ ., data = datos)
## 
## Coefficients:
##        (Intercept)                   X         PrecioCompt             Ingreso  
##         10.5806217          -0.0003284           0.0930031           0.0156505  
##         Publicidad           Poblacion              Precio   CalidadEstantMalo  
##          0.1238581           0.0002157          -0.0953564          -4.8520250  
## CalidadEstantMedio                Edad           Educacion              Urbano  
##         -2.8941221          -0.0461835          -0.0224532           0.1278481  
##                USA  
##         -0.1853717
summary(modelo)
## 
## Call:
## lm(formula = Ventas ~ ., data = datos)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8409 -0.6817  0.0127  0.6468  3.4684 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        10.5806217  0.6119800  17.289  < 2e-16 ***
## X                  -0.0003284  0.0004538  -0.724    0.470    
## PrecioCompt         0.0930031  0.0041583  22.366  < 2e-16 ***
## Ingreso             0.0156505  0.0018582   8.422  7.3e-16 ***
## Publicidad          0.1238581  0.0111803  11.078  < 2e-16 ***
## Poblacion           0.0002157  0.0003708   0.582    0.561    
## Precio             -0.0953564  0.0026727 -35.678  < 2e-16 ***
## CalidadEstantMalo  -4.8520250  0.1532252 -31.666  < 2e-16 ***
## CalidadEstantMedio -2.8941221  0.1309763 -22.097  < 2e-16 ***
## Edad               -0.0461835  0.0031894 -14.480  < 2e-16 ***
## Educacion          -0.0224532  0.0198208  -1.133    0.258    
## Urbano              0.1278481  0.1132532   1.129    0.260    
## USA                -0.1853717  0.1499447  -1.236    0.217    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.02 on 387 degrees of freedom
## Multiple R-squared:  0.8736, Adjusted R-squared:  0.8697 
## F-statistic: 222.9 on 12 and 387 DF,  p-value: < 2.2e-16
# Calcula el modelo usando solo los datos de training
modelo.lm <- lm(Ventas~., data = taprendizaje)
modelo.lm
## 
## Call:
## lm(formula = Ventas ~ ., data = taprendizaje)
## 
## Coefficients:
##        (Intercept)                   X         PrecioCompt             Ingreso  
##          1.052e+01          -3.119e-04           9.286e-02           1.587e-02  
##         Publicidad           Poblacion              Precio   CalidadEstantMalo  
##          1.167e-01           9.516e-05          -9.386e-02          -4.819e+00  
## CalidadEstantMedio                Edad           Educacion              Urbano  
##         -2.915e+00          -4.467e-02          -3.756e-02           1.972e-01  
##                USA  
##         -9.165e-02
numero.predictoras <- dim(datos)[2] - 1
# Hace la Predicción
prediccion <- predict(modelo.lm, ttesting)
# Medición de precisión
pre.lm <- indices.precision(ttesting$Ventas, prediccion,numero.predictoras)
pre.lm
## $error.cuadratico
## [1] 1.477647
## 
## $raiz.error.cuadratico
## [1] 1.359065
## 
## $error.relativo
## [1] 0.1423242
## 
## $correlacion
## [1] 0.9225625
# Gráfico real vs predicción, con curva de mejor ajuste lineal
g <- plot.real.prediccion(ttesting$Ventas, prediccion, modelo = "Regresión Lineal")
g + geom_smooth(method = lm, size = 0.4, color = "red", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

prediccion <- predict(modelo,datos, interval="confidence")
hist(prediccion, col = "green")

###Ridge

# La siguiente instrucción construye una matriz con los predictores
x<-model.matrix(Ventas~.,datos)
head(x)
##   (Intercept) X PrecioCompt Ingreso Publicidad Poblacion Precio
## 1           1 1         138      73         11       276    120
## 2           1 2         111      48         16       260     83
## 3           1 3         113      35         10       269     80
## 4           1 4         117     100          4       466     97
## 5           1 5         141      64          3       340    128
## 6           1 6         124     113         13       501     72
##   CalidadEstantMalo CalidadEstantMedio Edad Educacion Urbano USA
## 1                 1                  0   42        17      1   1
## 2                 0                  0   65        10      1   1
## 3                 0                  1   59        12      1   1
## 4                 0                  1   55        14      1   1
## 5                 1                  0   38        13      1   0
## 6                 1                  0   78        16      0   1
# Debemos eliminar la columna 1
x<-model.matrix(Ventas~.,datos)[,-c(1,2)]
head(x)
##   PrecioCompt Ingreso Publicidad Poblacion Precio CalidadEstantMalo
## 1         138      73         11       276    120                 1
## 2         111      48         16       260     83                 0
## 3         113      35         10       269     80                 0
## 4         117     100          4       466     97                 0
## 5         141      64          3       340    128                 1
## 6         124     113         13       501     72                 1
##   CalidadEstantMedio Edad Educacion Urbano USA
## 1                  0   42        17      1   1
## 2                  0   65        10      1   1
## 3                  1   59        12      1   1
## 4                  1   55        14      1   1
## 5                  0   38        13      1   0
## 6                  0   78        16      0   1
# La siguiente instrucción construye la variable a predecir
y<-datos$Ventas
library(glmnet)
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
## Loaded glmnet 4.0
ridge.mod<-glmnet(x,y,alpha=0)
dim(coef(ridge.mod))
## [1]  12 100
coef(ridge.mod)
## 12 x 100 sparse Matrix of class "dgCMatrix"
##    [[ suppressing 100 column names 's0', 's1', 's2' ... ]]
##                                                                           
## (Intercept)         7.496325e+00  7.510538e+00  7.511918e+00  7.513432e+00
## PrecioCompt         1.192041e-38  2.931185e-05  3.219135e-05  3.535601e-05
## Ingreso             1.548850e-38  3.770975e-05  4.137451e-05  4.539416e-05
## Publicidad          1.156036e-37  2.812402e-04  3.085487e-04  3.384969e-04
## Poblacion           9.769243e-40  2.369954e-06  2.599362e-06  2.850801e-06
## Precio             -5.360911e-38 -1.306493e-04 -1.433599e-04 -1.573041e-04
## CalidadEstantMalo  -2.622818e-36 -6.391766e-03 -7.013581e-03 -7.695742e-03
## CalidadEstantMedio -4.235720e-37 -1.038898e-03 -1.140677e-03 -1.252478e-03
## Edad               -4.081940e-38 -9.948302e-05 -1.091618e-04 -1.197800e-04
## Educacion          -5.655718e-38 -1.374580e-04 -1.507909e-04 -1.654097e-04
## Urbano             -9.633114e-38 -2.336091e-04 -2.562129e-04 -2.809854e-04
## USA                 1.054415e-36  2.561941e-03  2.810361e-03  3.082723e-03
##                                                                           
## (Intercept)         7.515093e+00  7.516913e+00  7.518910e+00  7.521098e+00
## PrecioCompt         3.883450e-05  4.265850e-05  4.686297e-05  5.148655e-05
## Ingreso             4.980280e-05  5.463778e-05  5.993993e-05  6.575397e-05
## Publicidad          3.713377e-04  4.073474e-04  4.468284e-04  4.901110e-04
## Poblacion           3.126349e-06  3.428275e-06  3.759052e-06  4.121376e-06
## Precio             -1.726010e-04 -1.893813e-04 -2.077879e-04 -2.279772e-04
## CalidadEstantMalo  -8.444076e-03 -9.264966e-03 -1.016540e-02 -1.115305e-02
## CalidadEstantMedio -1.375298e-03 -1.510237e-03 -1.658504e-03 -1.821433e-03
## Edad               -1.314284e-04 -1.442064e-04 -1.582228e-04 -1.735970e-04
## Educacion          -1.814369e-04 -1.990064e-04 -2.182644e-04 -2.393707e-04
## Urbano             -3.081310e-04 -3.378723e-04 -3.704522e-04 -4.061350e-04
## USA                 3.381306e-03  3.708598e-03  4.067317e-03  4.460429e-03
##                                                                           
## (Intercept)         7.523498e+00  7.526128e+00  7.529010e+00  7.532169e+00
## PrecioCompt         5.657196e-05  6.216643e-05  6.832228e-05  7.509741e-05
## Ingreso             7.212877e-05  7.911776e-05  8.677935e-05  9.517733e-05
## Publicidad          5.375563e-04  5.895585e-04  6.465481e-04  7.089946e-04
## Poblacion           4.518180e-06  4.952655e-06  5.428271e-06  5.948793e-06
## Precio             -2.501208e-04 -2.744064e-04 -3.010392e-04 -3.302441e-04
## CalidadEstantMalo  -1.223628e-02 -1.342427e-02 -1.472707e-02 -1.615567e-02
## CalidadEstantMedio -2.000497e-03 -2.197318e-03 -2.413687e-03 -2.651582e-03
## Edad               -1.904594e-04 -2.089531e-04 -2.292344e-04 -2.514745e-04
## Educacion          -2.624995e-04 -2.878409e-04 -3.156020e-04 -3.460084e-04
## Urbano             -4.452084e-04 -4.879851e-04 -5.348048e-04 -5.860359e-04
## USA                 4.891171e-03  5.363069e-03  5.879968e-03  6.446051e-03
##                                                                           
## (Intercept)         7.535631e+00  7.539423e+00  7.543576e+00  7.548125e+00
## PrecioCompt         8.255604e-05  9.076937e-05  9.981664e-05  1.097854e-04
## Ingreso             1.043814e-04  1.144674e-04  1.255186e-04  1.376250e-04
## Publicidad          7.774101e-04  8.523526e-04  9.344298e-04  1.024303e-03
## Poblacion           6.518306e-06  7.141236e-06  7.822373e-06  8.566882e-06
## Precio             -3.622668e-04 -3.973760e-04 -4.358655e-04 -4.780562e-04
## CalidadEstantMalo  -1.772207e-02 -1.943942e-02 -2.132208e-02 -2.338573e-02
## CalidadEstantMedio -2.913189e-03 -3.200923e-03 -3.517454e-03 -3.865738e-03
## Edad               -2.758605e-04 -3.025973e-04 -3.319084e-04 -3.640383e-04
## Educacion          -3.793059e-04 -4.157614e-04 -4.556653e-04 -4.993324e-04
## Urbano             -6.420775e-04 -7.033617e-04 -7.703553e-04 -8.435616e-04
## USA                 7.065870e-03  7.744373e-03  8.486930e-03  9.299368e-03
##                                                                           
## (Intercept)         7.553105e+00  7.558557e+00  7.564524e+00  7.571052e+00
## PrecioCompt         1.207735e-04  1.328897e-04  1.462551e-04  1.610048e-04
## Ingreso             1.508854e-04  1.654067e-04  1.813057e-04  1.987089e-04
## Publicidad          1.122690e-03  1.230373e-03  1.348198e-03  1.477085e-03
## Poblacion           9.380344e-06  1.026876e-05  1.123858e-05  1.229673e-05
## Precio             -5.242987e-04 -5.749755e-04 -6.305042e-04 -6.913399e-04
## CalidadEstantMalo  -2.564751e-02 -2.812612e-02 -3.084195e-02 -3.381724e-02
## CalidadEstantMedio -4.249048e-03 -4.671012e-03 -5.135649e-03 -5.647421e-03
## Edad               -3.992540e-04 -4.378468e-04 -4.801345e-04 -5.264638e-04
## Educacion          -5.471044e-04 -5.993511e-04 -6.564725e-04 -7.189005e-04
## Urbano             -9.235228e-04 -1.010821e-03 -1.106081e-03 -1.209968e-03
## USA                 1.018800e-02  1.115965e-02  1.222169e-02  1.338209e-02
##                                                                           
## (Intercept)         7.578192e+00  7.585999e+00  7.594533e+00  7.603857e+00
## PrecioCompt         1.772897e-04  1.952784e-04  2.151593e-04  2.371437e-04
## Ingreso             2.177541e-04  2.385903e-04  2.613792e-04  2.862956e-04
## Publicidad          1.618026e-03  1.772098e-03  1.940459e-03  2.124361e-03
## Poblacion           1.345058e-05  1.470802e-05  1.607742e-05  1.756764e-05
## Precio             -7.579787e-04 -8.309608e-04 -9.108742e-04 -9.983580e-04
## CalidadEstantMalo  -3.707624e-02 -4.064532e-02 -4.455321e-02 -4.883111e-02
## CalidadEstantMedio -6.211284e-03 -6.832742e-03 -7.517921e-03 -8.273635e-03
## Edad               -5.772121e-04 -6.327907e-04 -6.936467e-04 -7.602664e-04
## Educacion          -7.871006e-04 -8.615739e-04 -9.428584e-04 -1.031531e-03
## Urbano             -1.323196e-03 -1.446517e-03 -1.580732e-03 -1.726681e-03
## USA                 1.464940e-02  1.603282e-02  1.754223e-02  1.918816e-02
##                                                                           
## (Intercept)         7.614039e+00  7.625155e+00  7.637283e+00  7.650508e+00
## PrecioCompt         2.614681e-04  2.883982e-04  3.182319e-04  3.513045e-04
## Ingreso             3.135284e-04  3.432814e-04  3.757739e-04  4.112418e-04
## Publicidad          2.325149e-03  2.544268e-03  2.783264e-03  3.043791e-03
## Poblacion           1.918804e-05  2.094841e-05  2.285901e-05  2.493046e-05
## Precio             -1.094107e-03 -1.198874e-03 -1.313476e-03 -1.438799e-03
## CalidadEstantMalo  -5.351293e-02 -5.863545e-02 -6.423854e-02 -7.036539e-02
## CalidadEstantMedio -9.107477e-03 -1.002791e-02 -1.104438e-02 -1.216743e-02
## Edad               -8.331776e-04 -9.129528e-04 -1.000213e-03 -1.095628e-03
## Educacion          -1.128206e-03 -1.233542e-03 -1.348236e-03 -1.473025e-03
## Urbano             -1.885245e-03 -2.057342e-03 -2.243920e-03 -2.445952e-03
## USA                 2.098187e-02  2.293531e-02  2.506114e-02  2.737273e-02
##                                                                           
## (Intercept)         7.664921e+00  7.680618e+00  7.697701e+00  7.716278e+00
## PrecioCompt         3.879929e-04  4.287215e-04  4.739681e-04  5.242712e-04
## Ingreso             4.499380e-04  4.921330e-04  5.381159e-04  5.881940e-04
## Publicidad          3.327612e-03  3.636602e-03  3.972744e-03  4.338135e-03
## Poblacion           2.717371e-05  2.959994e-05  3.222046e-05  3.504659e-05
## Precio             -1.575799e-03 -1.725509e-03 -1.889045e-03 -2.067605e-03
## CalidadEstantMalo  -7.706265e-02 -8.438074e-02 -9.237398e-02 -1.011008e-01
## CalidadEstantMedio -1.340885e-02 -1.478180e-02 -1.630103e-02 -1.798302e-02
## Edad               -1.199924e-03 -1.313884e-03 -1.438348e-03 -1.574223e-03
## Educacion          -1.608689e-03 -1.756045e-03 -1.915946e-03 -2.089278e-03
## Urbano             -2.664421e-03 -2.900317e-03 -3.154612e-03 -3.428245e-03
## USA                 2.988411e-02  3.260995e-02  3.556548e-02  3.876643e-02
##                                                                           
## (Intercept)         7.736462e+00  7.758372e+00  7.782131e+00  7.807867e+00
## PrecioCompt         5.802380e-04  6.425529e-04  7.119871e-04  7.894104e-04
## Ingreso             6.426937e-04  7.019599e-04  7.663561e-04  8.362634e-04
## Publicidad          4.734977e-03  5.165576e-03  5.632334e-03  6.137742e-03
## Poblacion           3.808947e-05  4.135988e-05  4.486801e-05  4.862319e-05
## Precio             -2.262479e-03 -2.475050e-03 -2.706799e-03 -2.959305e-03
## CalidadEstantMalo  -1.106242e-01 -1.210113e-01 -1.323343e-01 -1.446700e-01
## CalidadEstantMedio -1.984626e-02 -2.191142e-02 -2.420169e-02 -2.674302e-02
## Edad               -1.722479e-03 -1.884153e-03 -2.060352e-03 -2.252253e-03
## Educacion          -2.276958e-03 -2.479919e-03 -2.699113e-03 -2.935494e-03
## Urbano             -3.722099e-03 -4.036968e-03 -4.373527e-03 -4.732291e-03
## USA                 4.222888e-02  4.596917e-02  5.000366e-02  5.434858e-02
##                                                                           
## (Intercept)         7.8357116809  7.865798e+00  7.898262e+00  7.9332365718
## PrecioCompt         0.0008758018  9.722637e-04  1.080035e-03  0.0012005081
## Ingreso             0.0009120799  9.942188e-04  1.083107e-03  0.0011791812
## Publicidad          0.0066843599  7.274810e-03  7.911751e-03  0.0085978577
## Poblacion           0.0000526336  5.690588e-05  6.144482e-05  0.0000662529
## Precio             -0.0032342511 -3.533425e-03 -3.858720e-03 -0.0042121333
## CalidadEstantMalo  -0.1581004478 -1.727125e-01 -1.885981e-01 -0.2058543399
## CalidadEstantMedio -0.0295644872 -3.269862e-02 -3.618183e-02 -0.0400547641
## Edad               -0.0024611021 -2.688216e-03 -2.934977e-03 -0.0032028317
## Educacion          -0.0031900082 -3.463579e-03 -3.757093e-03 -0.0040713788
## Urbano             -0.0051135647 -5.517391e-03 -5.943490e-03 -0.0063911865
## USA                 0.0590197108  6.403209e-02  6.939969e-02  0.0751349463
##                                                                          
## (Intercept)         7.970856e+00  8.011247e+00  8.054532625  8.100824e+00
## PrecioCompt         1.335241e-03  1.485978e-03  0.001654664  1.843463e-03
## Ingreso             1.282887e-03  1.394674e-03  0.001514989  1.644275e-03
## Publicidad          9.335793e-03  1.012818e-02  0.010977560  1.188637e-02
## Poblacion           7.132985e-05  7.667224e-05  0.000082273  8.812097e-05
## Precio             -4.595765e-03 -5.011815e-03 -0.005462577 -5.950432e-03
## CalidadEstantMalo  -2.245833e-01 -2.448918e-01 -0.266891131 -2.906970e-01
## CalidadEstantMedio -4.436281e-02 -4.915653e-02 -0.054492118 -6.043193e-02
## Edad               -3.493282e-03 -3.807880e-03 -0.004148219 -4.515915e-03
## Educacion          -4.407186e-03 -4.765164e-03 -0.005145832 -5.549555e-03
## Urbano             -6.859335e-03 -7.346234e-03 -0.007849539 -8.366162e-03
## USA                 8.124834e-02  8.774788e-02  0.094638498  1.019215e-01
##                                                                           
## (Intercept)         8.150220e+00  8.2028030720  8.2586390266  8.3177664043
## PrecioCompt         2.054773e-03  0.0022912595  0.0025558033  0.0028516005
## Ingreso             1.782962e-03  0.0019314606  0.0020901579  0.0022594040
## Publicidad          1.285687e-02  0.0138911595  0.0149910674  0.0161581526
## Poblacion           9.420053e-05  0.0001004911  0.0001069675  0.0001135988
## Precio             -6.477840e-03 -0.0070473216 -0.0076614530 -0.0083228396
## CalidadEstantMalo  -3.164286e-01 -0.3442084708 -0.3741617657 -0.4064151422
## CalidadEstantMedio -6.704493e-02 -0.0744071130 -0.0826019566 -0.0917207214
## Edad               -4.912595e-03 -0.0053398791 -0.0057993513 -0.0062925376
## Educacion          -5.976515e-03 -0.0064266805 -0.0068997760 -0.0073952583
## Urbano             -8.892177e-03 -0.0094227259 -0.0099519043 -0.0104727013
## USA                 1.095939e-01  0.1176476391  0.1260692605  0.1348388804
##                                                                          
## (Intercept)         8.3801972407  8.4459118279  8.5148547791  8.586931353
## PrecioCompt         0.0031821124  0.0035510792  0.0039625137  0.004420687
## Ingreso             0.0024395069  0.0026307231  0.0028332480  0.003047207
## Publicidad          0.0173936481  0.0186984254  0.0200729614  0.021517312
## Poblacion           0.0001203492  0.0001271779  0.0001340396  0.000140885
## Precio             -0.0090340996 -0.0097978386 -0.0106166228 -0.011492948
## CalidadEstantMalo  -0.4410960784 -0.4783317242 -0.5182476851 -0.560966670
## CalidadEstantMedio -0.1018626915 -0.1131352736 -0.1256539260 -0.139541879
## Edad               -0.0068208739 -0.0073856719 -0.0079880818 -0.008629052
## Educacion          -0.0079122888 -0.0084497127 -0.0090060432 -0.009579452
## Urbano             -0.0109769088 -0.0114550708 -0.0118964526 -0.012289044
## USA                 0.1439297804  0.1533078211  0.1629309924  0.172749087
##                                                                          
## (Intercept)         8.6620042049  8.739890751  8.8203613374  8.9031384086
## PrecioCompt         0.0049301042  0.005495471  0.0061216466  0.0068135829
## Ingreso             0.0032726491  0.003509534  0.0037577306  0.0040170076
## Publicidad          0.0230310950  0.024613479  0.0262631886  0.0279785129
## Poblacion           0.0001476619  0.000154316  0.0001607928  0.0001670383
## Precio             -0.0124292053 -0.013427646 -0.0144903386 -0.0156191282
## CalidadEstantMalo  -0.6066070021 -0.655281005 -0.7070932529 -0.7621387127
## CalidadEstantMedio -0.1549296051 -0.171953999 -0.1907572294 -0.2114852300
## Edad               -0.0093092872 -0.010029205 -0.0107888929 -0.0115880685
## Educacion          -0.0101677690 -0.010768488 -0.0113787889 -0.0119955616
## Urbano             -0.0126195983 -0.012873720 -0.0130359974 -0.0130901944
## USA                 0.1827035285  0.192727383  0.2027455769  0.2126753407
##                                                                           
## (Intercept)         8.9878968458  9.0742656377  9.1618309924  9.2501409573
## PrecioCompt         0.0075762535  0.0084145661  0.0093332634  0.0103368117
## Ingreso             0.0042870316  0.0045673637  0.0048574597  0.0051566725
## Publicidad          0.0297573313  0.0315971447  0.0334951164  0.0354481195
## Poblacion           0.0001730015  0.0001786356  0.0001838996  0.0001887601
## Precio             -0.0168155897 -0.0180809809 -0.0194161931 -0.0208217016
## CalidadEstantMalo  -0.8205007638 -0.8822491228 -0.9474376766 -1.0161022439
## CalidadEstantMedio -0.2342858084 -0.2593063575 -0.2866911746 -0.3165784079
## Edad               -0.0124260414 -0.0133016821 -0.0142133984 -0.0151591208
## Educacion          -0.0126154499 -0.0132349002 -0.0138502207 -0.0144576502
## Urbano             -0.0130194924 -0.0128067890 -0.0124350455 -0.0118876767
## USA                 0.2224268969  0.2319043909  0.2410070640  0.2496306567
##                                                                           
## (Intercept)         9.3387115455  9.4270342973  9.5145851268  9.6008342224
## PrecioCompt         0.0114292798  0.0126142107  0.0138944899  0.0152722140
## Ingreso             0.0054642560  0.0057793722  0.0061010996  0.0064284441
## Publicidad          0.0374527867  0.0395055612  0.0416027450  0.0437405414
## Poblacion           0.0001931925  0.0001971819  0.0002007237  0.0002038235
## Precio             -0.0222975156 -0.0238431299 -0.0254574787 -0.0271388939
## CalidadEstantMalo  -1.0882582905 -1.1638986283 -1.2429911439 -1.3254766122
## CalidadEstantMedio -0.3490966724 -0.3843613959 -0.4224709835 -0.4635029070
## Edad               -0.0161363000 -0.0171419164 -0.0181725036 -0.0192241860
## Educacion          -0.0150534313 -0.0156338873 -0.0161955013 -0.0167349910
## Urbano             -0.0111489723 -0.0102045366 -0.0090417298 -0.0076500935
## USA                 0.2576690157  0.2650158776  0.2715667861  0.2772210988
##                                                                           
## (Intercept)         9.6851963735  9.7672611876  9.8465023120  9.9224742713
## PrecioCompt         0.0167491571  0.0183245168  0.0199977496  0.0217666876
## Ingreso             0.0067603712  0.0070957482  0.0074334550  0.0077723401
## Publicidad          0.0459147206  0.0481220010  0.0503581833  0.0526193155
## Poblacion           0.0002065025  0.0002087775  0.0002106845  0.0002122622
## Precio             -0.0288851893 -0.0306932073 -0.0325593749 -0.0344793487
## CalidadEstantMalo  -1.4112722868 -1.5002509856 -1.5922648474 -1.6871293360
## CalidadEstantMedio -0.5075112210 -0.5545183633 -0.6045177526 -0.6574675323
## Edad               -0.0202927131 -0.0213735821 -0.0224620315 -0.0235531724
## Educacion          -0.0172493442 -0.0177360161 -0.0181927945 -0.0186179571
## Urbano             -0.0060221608 -0.0041522198 -0.0020387964  0.0003165434
## USA                 0.2818855467  0.2854707625  0.2879007484  0.2891100805
##                                                                           
## (Intercept)         9.9947787748 10.0630730945 10.1270767716 10.1865763691
## PrecioCompt         0.0236279067  0.0255767064  0.0276071227  0.0297119778
## Ingreso             0.0081112482  0.0084490350  0.0087845798  0.0091167980
## Publicidad          0.0549014116  0.0572004324  0.0595122600  0.0618326681
## Poblacion           0.0002135534  0.0002146024  0.0002154538  0.0002161495
## Precio             -0.0364481029 -0.0384599523 -0.0405085925 -0.0425871578
## CalidadEstantMalo  -1.7846262002 -1.8845037068 -1.9864776376 -2.0902331282
## CalidadEstantMedio -0.7132892361 -0.7718664859 -0.8330446245 -0.8966313685
## Edad               -0.0246420654 -0.0257238097 -0.0267936313 -0.0278469663
## Educacion          -0.0190102712 -0.0193690056 -0.0196939279 -0.0199852879
## Urbano              0.0029087729  0.0057294073  0.0087666070  0.0120053779
## USA                 0.2890466064  0.2876728350  0.2849670385  0.2809240247
##                                                                           
## (Intercept)        10.2414280884 10.2915581772 10.3369611765 10.3776961624
## PrecioCompt         0.0318829650  0.0341107688  0.0363852169  0.0386954583
## Ingreso             0.0094446525  0.0097671626  0.0100834128  0.0103925599
## Publicidad          0.0641572931  0.0664816090  0.0688009106  0.0711103076
## Poblacion           0.0002167278  0.0002172218  0.0002176585  0.0002180584
## Precio             -0.0446882953 -0.0468042565 -0.0489270023 -0.0510483207
## CalidadEstantMalo  -2.1954273921 -2.3016933354 -2.4086440218 -2.5158778954
## CalidadEstantMedio -0.9623985316 -1.0300848186 -1.0993996394 -1.1700278431
## Edad               -0.0288795367 -0.0298874152 -0.0308670785 -0.0318154469
## Educacion          -0.0202437871 -0.0204705369 -0.0206670072 -0.0208349678
## Urbano              0.0154278604  0.0190136978  0.0227404678  0.0265841617
## USA                 0.2755555448  0.2688903113  0.2609736119  0.2518665208
##                                                                           
## (Intercept)        10.4138812396 10.4456866178 10.4733266560 10.4967780611
## PrecioCompt         0.0410301627  0.0433777322  0.0457265192  0.0480685281
## Ingreso             0.0106938378  0.0109865624  0.0112701344  0.0115441968
## Publicidad          0.0734047328  0.0756789654  0.0779276689  0.0801462938
## Poblacion           0.0002184354  0.0002187976  0.0002191476  0.0002195204
## Precio             -0.0531599512 -0.0552537145 -0.0573216412 -0.0593572904
## CalidadEstantMalo  -2.6229846251 -2.7295513879 -2.8351693800 -2.9395420834
## CalidadEstantMedio -1.2416352212 -1.3138745890 -1.3863922240 -1.4588791109
## Edad               -0.0327299096 -0.0336083370 -0.0344490797 -0.0352508512
## Educacion          -0.0209764251 -0.0210935573 -0.0211886499 -0.0212642429
## Urbano              0.0305196934  0.0345214177  0.0385636426  0.0426232983
## USA                 0.2416447207  0.2303969664  0.2182232366  0.2052239100
##                                                                           
## (Intercept)        10.5168306399 10.5335403245 10.5472125932 10.5581553567
## PrecioCompt         0.0503861712  0.0526718803  0.0549158043  0.0571089458
## Ingreso             0.0118080349  0.0120614432  0.0123041674  0.0125360362
## Publicidad          0.0823282390  0.0844686292  0.0865622942  0.0886043008
## Poblacion           0.0002198406  0.0002201339  0.0002203921  0.0002206067
## Precio             -0.0613512791 -0.0632979749 -0.0651913701 -0.0670261585
## CalidadEstantMalo  -3.0421029229 -3.1425770958 -3.2406329801 -3.3359709405
## CalidadEstantMedio -1.5309079711 -1.6021800571 -1.6723784418 -1.7412089729
## Edad               -0.0360131077 -0.0367354385 -0.0374179003 -0.0380608875
## Educacion          -0.0213223313 -0.0213653087 -0.0213953333 -0.0214144278
## Urbano              0.0466722863  0.0506891306  0.0546523490  0.0585423424
## USA                 0.1915280179  0.1772507544  0.1625146393  0.1474419363
##                                                                         
## (Intercept)        10.56667233 10.5730574895 10.5775906354 10.5805340156
## PrecioCompt         0.05924327  0.0613117631  0.0633085053  0.0652286528
## Ingreso             0.01275696  0.0129669141  0.0131659594  0.0133542111
## Publicidad          0.09059003  0.0925152718  0.0943762520  0.0961697263
## Poblacion           0.00022077  0.0002208758  0.0002209193  0.0002208981
## Precio             -0.06879778 -0.0705024633 -0.0721372024 -0.0736997777
## CalidadEstantMalo  -3.42832674 -3.5174739173 -3.6032251662 -3.6854326851
## CalidadEstantMedio -1.80840422 -1.8737264837 -1.9369698497 -1.9979612914
## Edad               -0.03866509 -0.0392314458 -0.0397611059 -0.0402553856
## Educacion          -0.02142446 -0.0214271041 -0.0214238768 -0.0214160920
## Urbano              0.06234160  0.0660348059  0.0696089514  0.0730532916
## USA                 0.13215244  0.1167614786  0.1013781117  0.0861036986
##                                                                           
## (Intercept)        10.5821299855 10.5825995889 10.5821419466 10.5809343217
## PrecioCompt         0.0670684386  0.0688251314  0.0704969783  0.0720831324
## Ingreso             0.0135318454  0.0136990905  0.0138562201  0.0140035461
## Publicidad          0.0978930010  0.0995439627  0.1011210884  0.1026234419
## Poblacion           0.0002208114  0.0002206606  0.0002204487  0.0002201801
## Precio             -0.0751887138 -0.0766032424 -0.0779432507 -0.0792092224
## CalidadEstantMalo  -3.7639876289 -3.8388187317 -3.9098902373 -3.9771992770
## CalidadEstantMedio -2.0565608900 -2.1126612513 -2.1661862318 -2.2170890991
## Edad               -0.0407157300 -0.0411436777 -0.0415408299 -0.0419088224
## Educacion          -0.0214048870 -0.0213912271 -0.0213759177 -0.0213596191
## Urbano              0.0763593191  0.0795206645  0.0825329645  0.0853937027
## USA                 0.0710307232  0.0562419675  0.0418099981  0.0277969504
##                                                                           
## (Intercept)        10.5791327270 10.5768729459 10.5742718499 10.5714289071
## PrecioCompt         0.0735835694  0.0749989974  0.0763307630  0.0775807581
## Ingreso             0.0141414129  0.0142701900  0.0143902664  0.0145020448
## Publicidad          0.1040506567  0.1054029067  0.1066808682  0.1078856740
## Poblacion           0.0002198604  0.0002194958  0.0002190933  0.0002186597
## Precio             -0.0804021722 -0.0815235783 -0.0825753134 -0.0835595779
## CalidadEstantMalo  -4.0407728382 -4.1006644622 -4.1569508023 -4.2097281556
## CalidadEstantMedio -2.2653502543 -2.3109746456 -2.3539889921 -2.3944389246
## Edad               -0.0422493016 -0.0425639044 -0.0428542414 -0.0431218835
## Educacion          -0.0213428621 -0.0213260641 -0.0213095451 -0.0212935426
## Urbano              0.0881020320  0.0906585848  0.0930652804  0.0953251338
## USA                 0.0142545837  0.0012245686 -0.0112610317 -0.0231791283
##                                                                           
## (Intercept)        10.5684277961 10.5657421854 10.5626038367 10.5594777545
## PrecioCompt         0.0787513282  0.0798384668  0.0808588223  0.0818087290
## Ingreso             0.0146059360  0.0147020299  0.0147913988  0.0148741215
## Publicidad          0.1090188629  0.1100726602  0.1110685497  0.1119995477
## Poblacion           0.0002182022  0.0002177199  0.0002172375  0.0002167489
## Precio             -0.0844788348 -0.0853330298 -0.0861304920 -0.0868713583
## CalidadEstantMalo  -4.2591090667 -4.3049580515 -4.3479389818 -4.3879301387
## CalidadEstantMedio -2.4323861361 -2.4677608873 -2.5009409724 -2.5318747269
## Edad               -0.0433683508 -0.0435953393 -0.0438037721 -0.0439952156
## Educacion          -0.0212782259 -0.0212619424 -0.0212482836 -0.0212355679
## Urbano              0.0974420715  0.0994116274  0.1012574617  0.1029760907
## USA                -0.0345148630 -0.0451654906 -0.0553201587 -0.0648910487
ridge.mod$lambda
##   [1] 1255.0203235 1143.5277770 1041.9399211  949.3768503  865.0368276
##   [6]  788.1893400  718.1687715  654.3686372  596.2363311  543.2683388
##  [11]  495.0058769  451.0309191  410.9625754  374.4537929  341.1883500
##  [16]  310.8781174  283.2605623  258.0964746  235.1678952  214.2762275
##  [21]  195.2405181  177.8958886  162.0921081  147.6922920  134.5717159
##  [26]  122.6167355  111.7238027  101.7985680   92.7550638   84.5149596
##  [31]   77.0068835   70.1658042   63.9324676   58.2528834   53.0778578
##  [36]   48.3625672   44.0661700   40.1514529   36.5845085   33.3344416
##  [41]   30.3731017   27.6748390   25.2162826   22.9761376   20.9350008
##  [46]   19.0751931   17.3806055   15.8365604   14.4296840   13.1477907
##  [51]   11.9797772   10.9155268    9.9458215    9.0622621    8.2571957
##  [56]    7.5236492    6.8552690    6.2462658    5.6913647    5.1857596
##  [61]    4.7250710    4.3053087    3.9228369    3.5743429    3.2568081
##  [66]    2.9674823    2.7038593    2.4636559    2.2447915    2.0453704
##  [71]    1.8636654    1.6981025    1.5472477    1.4097945    1.2845522
##  [76]    1.1704362    1.0664578    0.9717167    0.8853920    0.8067362
##  [81]    0.7350680    0.6697666    0.6102664    0.5560520    0.5066539
##  [86]    0.4616441    0.4206329    0.3832651    0.3492169    0.3181934
##  [91]    0.2899260    0.2641698    0.2407016    0.2193184    0.1998347
##  [96]    0.1820820    0.1659063    0.1511676    0.1377383    0.1255020
plot(ridge.mod,"lambda", label=TRUE)

ridge.mod$lambda[5]
## [1] 865.0368
log(ridge.mod$lambda[5])
## [1] 6.762772
coef(ridge.mod)[,5]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##       7.515093e+00       3.883450e-05       4.980280e-05       3.713377e-04 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##       3.126349e-06      -1.726010e-04      -8.444076e-03      -1.375298e-03 
##               Edad          Educacion             Urbano                USA 
##      -1.314284e-04      -1.814369e-04      -3.081310e-04       3.381306e-03
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(ridge.mod$lambda[5]), col="blue", lwd=4, lty=3)

log(ridge.mod$lambda[70])
## [1] 0.7155789
coef(ridge.mod)[,70]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##      10.0630730945       0.0255767064       0.0084490350       0.0572004324 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##       0.0002146024      -0.0384599523      -1.8845037068      -0.7718664859 
##               Edad          Educacion             Urbano                USA 
##      -0.0257238097      -0.0193690056       0.0057294073       0.2876728350
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(ridge.mod$lambda[70]), col="blue", lwd=4, lty=3)

datosx<-model.matrix(Ventas~.,datos)[,-c(1,2)]
pred<-predict(ridge.mod,s=ridge.mod$lambda[5],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 7.927364
## 
## $raiz.error.cuadratico
## [1] 2.855089
## 
## $error.relativo
## [1] 0.3012258
## 
## $correlacion
## [1] 0.7610886
pred<-predict(ridge.mod,s=ridge.mod$lambda[70],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 3.453121
## 
## $raiz.error.cuadratico
## [1] 1.884348
## 
## $error.relativo
## [1] 0.1981819
## 
## $correlacion
## [1] 0.8924282
# Usando validación cruzada para determinar el mejor Lambda
sal.cv<-cv.glmnet(x,y,alpha=0)
plot(sal.cv)

mejor.lambda<-sal.cv$lambda.min
mejor.lambda
## [1] 0.125502
log(mejor.lambda)
## [1] -2.075433
coef(ridge.mod)[,which(ridge.mod$lambda==mejor.lambda)]
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##      10.5594777545       0.0818087290       0.0148741215       0.1119995477 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##       0.0002167489      -0.0868713583      -4.3879301387      -2.5318747269 
##               Edad          Educacion             Urbano                USA 
##      -0.0439952156      -0.0212355679       0.1029760907      -0.0648910487
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(mejor.lambda), col="blue", lwd=4, lty=3)

pred<-predict(ridge.mod,s=mejor.lambda,newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 1.065489
## 
## $raiz.error.cuadratico
## [1] 1.046718
## 
## $error.relativo
## [1] 0.1096337
## 
## $correlacion
## [1] 0.9340148

###LASSO

# Debemos eliminar la columna 1
x<-model.matrix(Ventas~.,datos)[,-c(1,2)]
head(x)
##   PrecioCompt Ingreso Publicidad Poblacion Precio CalidadEstantMalo
## 1         138      73         11       276    120                 1
## 2         111      48         16       260     83                 0
## 3         113      35         10       269     80                 0
## 4         117     100          4       466     97                 0
## 5         141      64          3       340    128                 1
## 6         124     113         13       501     72                 1
##   CalidadEstantMedio Edad Educacion Urbano USA
## 1                  0   42        17      1   1
## 2                  0   65        10      1   1
## 3                  1   59        12      1   1
## 4                  1   55        14      1   1
## 5                  0   38        13      1   0
## 6                  0   78        16      0   1
# La siguiente instrucción construye la variable a predecir
y<-datos$Ventas
library(glmnet)

lasso.mod<-glmnet(x,y,alpha=1) 
dim(coef(lasso.mod))
## [1] 12 63
coef(lasso.mod)
## 12 x 63 sparse Matrix of class "dgCMatrix"
##    [[ suppressing 63 column names 's0', 's1', 's2' ... ]]
##                                                                              
## (Intercept)        7.496325  8.042282293  8.595065622  9.11933418  9.59702816
## PrecioCompt        .         .            .            .           .         
## Ingreso            .         .            .            .           .         
## Publicidad         .         .            .            .           .         
## Poblacion          .         .            .            .           .         
## Precio             .        -0.004714861 -0.009125493 -0.01318696 -0.01688762
## CalidadEstantMalo  .         .           -0.175225562 -0.40010391 -0.60500469
## CalidadEstantMedio .         .            .            .           .         
## Edad               .         .            .            .           .         
## Educacion          .         .            .            .           .         
## Urbano             .         .            .            .           .         
## USA                .         .            .            .           .         
##                                                                       
## (Intercept)        10.03228512 10.4259716101 11.020131312 11.561507854
## PrecioCompt         .           .             .            .          
## Ingreso             .           .             .            .          
## Publicidad          .           0.0078665024  0.017577019  0.026424902
## Poblacion           .           .             .            .          
## Precio             -0.02025952 -0.0234725436 -0.026716715 -0.029672687
## CalidadEstantMalo  -0.79170265 -0.9588454343 -1.117097739 -1.261291390
## CalidadEstantMedio  .           .             .            .          
## Edad                .          -0.0006322802 -0.005226018 -0.009411661
## Educacion           .           .             .            .          
## Urbano              .           .             .            .          
## USA                 .           .             .            .          
##                                                                      
## (Intercept)        11.704683428 11.69977844 11.69549696 11.6622772936
## PrecioCompt         0.004810848  0.01243838  0.01938676  0.0257472917
## Ingreso             .            .           .           0.0003609279
## Publicidad          0.034849758  0.04240721  0.04929242  0.0554701330
## Poblacion           .            .           .           .           
## Precio             -0.034218275 -0.03967847 -0.04465313 -0.0491725061
## CalidadEstantMalo  -1.442750566 -1.73813979 -2.00735887 -2.2543339108
## CalidadEstantMedio -0.073816801 -0.32413307 -0.55224940 -0.7599784361
## Edad               -0.012974679 -0.01594313 -0.01864796 -0.0211071527
## Educacion           .            .           .           .           
## Urbano              .            .           .           .           
## USA                 .            .           .           .           
##                                                                      
## (Intercept)        11.54175589 11.432409927 11.332778164 11.241997411
## PrecioCompt         0.03168634  0.037095205  0.042023563  0.046514100
## Ingreso             0.00173055  0.002979105  0.004116743  0.005153316
## Publicidad          0.06084223  0.065733716  0.070190652  0.074251647
## Poblacion           .           .            .            .          
## Precio             -0.05327244 -0.057007731 -0.060411194 -0.063512303
## CalidadEstantMalo  -2.48327718 -2.692257203 -2.882672510 -3.056171846
## CalidadEstantMedio -0.94844387 -1.120364842 -1.277013085 -1.419745131
## Edad               -0.02332962 -0.025354919 -0.027200300 -0.028881742
## Educacion           .           .            .            .          
## Urbano              .           .            .            .          
## USA                 .           .            .            .          
##                                                                       
## (Intercept)        11.159281371 11.083913595 11.015241285 10.952669634
## PrecioCompt         0.050605709  0.054333831  0.057730757  0.060825910
## Ingreso             0.006097803  0.006958384  0.007742514  0.008456983
## Publicidad          0.077951875  0.081323384  0.084395378  0.087194464
## Poblacion           .            .            .            .          
## Precio             -0.066337918 -0.068912513 -0.071258388 -0.073395862
## CalidadEstantMalo  -3.214257979 -3.358300176 -3.489546061 -3.609132423
## CalidadEstantMedio -1.549797256 -1.668295908 -1.776267466 -1.874647129
## Edad               -0.030413810 -0.031809772 -0.033081722 -0.034240675
## Educacion           .            .            .            .          
## Urbano              .            .            .            .          
## USA                 .            .            .            .          
##                                                                     
## (Intercept)        10.895656676 10.844523277 10.79712287 10.75392829
## PrecioCompt         0.063646098  0.066203783  0.06854615  0.07068049
## Ingreso             0.009107982  0.009700824  0.01024132  0.01073380
## Publicidad          0.089744887  0.092068154  0.09418560  0.09611495
## Poblacion           .            .            .           .         
## Precio             -0.075343449 -0.077113100 -0.07873043 -0.08020411
## CalidadEstantMalo  -3.718095063 -3.817091853 -3.90757916 -3.99002862
## CalidadEstantMedio -1.964287014 -2.045804297 -2.12023859 -2.18806081
## Edad               -0.035296669 -0.036259201 -0.03713588 -0.03793467
## Educacion           .            .            .           .         
## Urbano              .            .            .           .         
## USA                 .            .            .           .         
##                                                                               
## (Intercept)        10.71457096 10.67871004 10.64603489 10.61626252 10.58913504
## PrecioCompt         0.07262522  0.07439719  0.07601175  0.07748287  0.07882330
## Ingreso             0.01118254  0.01159140  0.01196395  0.01230340  0.01261269
## Publicidad          0.09787290  0.09947468  0.10093417  0.10226399  0.10347568
## Poblacion           .           .           .           .           .         
## Precio             -0.08154687 -0.08277034 -0.08388513 -0.08490088 -0.08582639
## CalidadEstantMalo  -4.06515349 -4.13360447 -4.19597446 -4.25280367 -4.30458433
## CalidadEstantMedio -2.24985789 -2.30616508 -2.35747010 -2.40421732 -2.44681165
## Edad               -0.03866250 -0.03932567 -0.03992993 -0.04048051 -0.04098217
## Educacion           .           .           .           .           .         
## Urbano              .           .           .           .           .         
## USA                 .           .           .           .           .         
##                                                                    
## (Intercept)        10.56441750 10.54189579 10.52137485 10.527800397
## PrecioCompt         0.08004465  0.08115749  0.08217148  0.083100135
## Ingreso             0.01289451  0.01315129  0.01338526  0.013589591
## Publicidad          0.10457972  0.10558569  0.10650229  0.107315333
## Poblacion           .           .           .           .          
## Precio             -0.08666968 -0.08743806 -0.08813818 -0.088775734
## CalidadEstantMalo  -4.35176494 -4.39475415 -4.43392432 -4.469399500
## CalidadEstantMedio -2.48562202 -2.52098458 -2.55320562 -2.582359710
## Edad               -0.04143927 -0.04185576 -0.04223525 -0.042578835
## Educacion           .           .           .          -0.001819095
## Urbano              .           .           .           .          
## USA                 .           .           .           .          
##                                                                        
## (Intercept)        10.534674429 10.536397694 10.539466796  1.053260e+01
## PrecioCompt         0.083940025  0.084710773  0.085381970  8.602881e-02
## Ingreso             0.013774392  0.013938763  0.014087180  1.422654e-02
## Publicidad          0.108058654  0.108710009  0.109295829  1.097175e-01
## Poblacion           .            .            .            1.928014e-05
## Precio             -0.089353292 -0.089891814 -0.090371874 -9.082042e-02
## CalidadEstantMalo  -4.501114221 -4.531584233 -4.559073542 -4.584924e+00
## CalidadEstantMedio -2.608592702 -2.633046180 -2.655109707 -2.675443e+00
## Edad               -0.042891792 -0.043186309 -0.043457369 -4.369634e-02
## Educacion          -0.003539897 -0.005056452 -0.006428301 -7.569676e-03
## Urbano              .            0.008779033  0.018366597  2.749760e-02
## USA                 .            .            .            .           
##                                                                           
## (Intercept)         1.052362e+01  1.051529e+01  1.050770e+01 10.5007751572
## PrecioCompt         8.661940e-02  8.716091e-02  8.765438e-02  0.0881040153
## Ingreso             1.435459e-02  1.447137e-02  1.457779e-02  0.0146747446
## Publicidad          1.100650e-01  1.103812e-01  1.106693e-01  0.1109318534
## Poblacion           4.319623e-05  6.502944e-05  8.492402e-05  0.0001030512
## Precio             -9.122781e-02 -9.160067e-02 -9.194043e-02 -0.0922500097
## CalidadEstantMalo  -4.608134e+00 -4.629502e+00 -4.648972e+00 -4.6667135569
## CalidadEstantMedio -2.693720e+00 -2.710490e+00 -2.725771e+00 -2.7396945714
## Edad               -4.391155e-02 -4.410761e-02 -4.428625e-02 -0.0444490126
## Educacion          -8.575171e-03 -9.490611e-03 -1.032472e-02 -0.0110847248
## Urbano              3.592631e-02  4.361529e-02  5.062123e-02  0.0570047915
## USA                 .             .             .             .           
##                                                                           
## (Intercept)        10.4951315886 10.4967381739 10.4979909469 10.4991293375
## PrecioCompt         0.0885137054  0.0888935427  0.0892420065  0.0895594561
## Ingreso             0.0147630903  0.0148569756  0.0149409987  0.0150175618
## Publicidad          0.1111710615  0.1122735951  0.1132365569  0.1141124228
## Poblacion           0.0001195681  0.0001270701  0.0001342338  0.0001407755
## Precio             -0.0925320856 -0.0927816422 -0.0930105205 -0.0932190581
## CalidadEstantMalo  -4.6828785828 -4.6976972800 -4.7112451073 -4.7235880182
## CalidadEstantMedio -2.7523811287 -2.7649378404 -2.7763585794 -2.7867623558
## Edad               -0.0445973199 -0.0447256752 -0.0448428818 -0.0449496886
## Educacion          -0.0117772148 -0.0126157061 -0.0133699702 -0.0140568624
## Urbano              0.0628212504  0.0681611118  0.0730227956  0.0774525407
## USA                -0.0010277601 -0.0177122566 -0.0325083866 -0.0459753121
##                                                                           
## (Intercept)        10.5001666357 10.5017566971 10.5025972098 10.5033284005
## PrecioCompt         0.0898487003  0.0901013109  0.0903418061  0.0905615133
## Ingreso             0.0150873234  0.0151507369  0.0152086714  0.0152614559
## Publicidad          0.1149104204  0.1156045967  0.1162676480  0.1168740042
## Poblacion           0.0001467366  0.0001523542  0.0001573036  0.0001617978
## Precio             -0.0934090686 -0.0935780503 -0.0937359520 -0.0938800312
## CalidadEstantMalo  -4.7348343448 -4.7448398753 -4.7541909209 -4.7627182614
## CalidadEstantMedio -2.7962417868 -2.8047119621 -2.8125901871 -2.8197747826
## Edad               -0.0450470075 -0.0451362431 -0.0452170272 -0.0452905990
## Educacion          -0.0146827192 -0.0152459760 -0.0157656482 -0.0162396598
## Urbano              0.0814887592  0.0851647309  0.0885161357  0.0915695199
## USA                -0.0582453132 -0.0691066250 -0.0792989879 -0.0886069245
##                                                                           
## (Intercept)        10.5039927776 10.5045980317 10.5051495108 10.5056519977
## PrecioCompt         0.0907617352  0.0909441718  0.0911104014  0.0912618636
## Ingreso             0.0153095507  0.0153533728  0.0153933019  0.0154296838
## Publicidad          0.1174266553  0.1179302224  0.1183890548  0.1188071258
## Poblacion           0.0001658914  0.0001696213  0.0001730199  0.0001761165
## Precio             -0.0940113220 -0.0941309499 -0.0942399504 -0.0943392677
## CalidadEstantMalo  -4.7704883443 -4.7775681709 -4.7840190464 -4.7898968440
## CalidadEstantMedio -2.8263214529 -2.8322865569 -2.8377217391 -2.8426740748
## Edad               -0.0453576325 -0.0454187109 -0.0454743631 -0.0455250714
## Educacion          -0.0166715993 -0.0170651694 -0.0174237759 -0.0177505249
## Urbano              0.0943516264  0.0968865768  0.0991963291  0.1013008894
## USA                -0.0970895067 -0.1048186326 -0.1118611327 -0.1182779971
##                                                                         
## (Intercept)        10.506109845 10.5073834588 10.507816930 10.5081004410
## PrecioCompt         0.091399870  0.0915119196  0.091625799  0.0917312149
## Ingreso             0.015462834  0.0154928221  0.015520368  0.0155454647
## Publicidad          0.119188057  0.1195029926  0.119817528  0.1201080339
## Poblacion           0.000178938  0.0001816507  0.000184006  0.0001861307
## Precio             -0.094429762 -0.0945069321 -0.094581829 -0.0946506809
## CalidadEstantMalo  -4.795252474 -4.7998404442 -4.804292859 -4.8083680572
## CalidadEstantMedio -2.847186459 -2.8511042389 -2.854851694 -2.8582806552
## Edad               -0.045571275 -0.0456140129 -0.045652416 -0.0456873208
## Educacion          -0.018048246 -0.0183129302 -0.018559655 -0.0187853401
## Urbano              0.103218486  0.1049643564  0.106557745  0.1080085853
## USA                -0.124124805 -0.1291398925 -0.133978268 -0.1384240505
##                                                                           
## (Intercept)        10.5083432064 10.5085623004 10.5087616483 10.5089432488
## PrecioCompt         0.0918274906  0.0919152439  0.0919952055  0.0920680641
## Ingreso             0.0155683294  0.0155891624  0.0156081446  0.0156254405
## Publicidad          0.1203732988  0.1206150810  0.1208353959  0.1210361405
## Poblacion           0.0001880633  0.0001898236  0.0001914274  0.0001928887
## Precio             -0.0947134975 -0.0947707445 -0.0948229072 -0.0948704361
## CalidadEstantMalo  -4.8120826128 -4.8154673041 -4.8185513230 -4.8213613680
## CalidadEstantMedio -2.8614064043 -2.8642546345 -2.8668498574 -2.8692145308
## Edad               -0.0457191126 -0.0457480783 -0.0457744705 -0.0457985181
## Educacion          -0.0189911085 -0.0191786166 -0.0193494699 -0.0195051455
## Urbano              0.1093303268  0.1105346159  0.1116319148  0.1126317320
## USA                -0.1424802285 -0.1461768441 -0.1495451760 -0.1526142910
##                                 
## (Intercept)        10.5101802475
## PrecioCompt         0.0921175783
## Ingreso             0.0156407178
## Publicidad          0.1211836561
## Poblacion           0.0001942996
## Precio             -0.0949070828
## CalidadEstantMalo  -4.8235162298
## CalidadEstantMedio -2.8711100334
## Edad               -0.0458211287
## Educacion          -0.0196404504
## Urbano              0.1135363121
## USA                -0.1550627296
lasso.mod$lambda
##  [1] 1.255020323 1.143527777 1.041939921 0.949376850 0.865036828 0.788189340
##  [7] 0.718168771 0.654368637 0.596236331 0.543268339 0.495005877 0.451030919
## [13] 0.410962575 0.374453793 0.341188350 0.310878117 0.283260562 0.258096475
## [19] 0.235167895 0.214276228 0.195240518 0.177895889 0.162092108 0.147692292
## [25] 0.134571716 0.122616736 0.111723803 0.101798568 0.092755064 0.084514960
## [31] 0.077006884 0.070165804 0.063932468 0.058252883 0.053077858 0.048362567
## [37] 0.044066170 0.040151453 0.036584508 0.033334442 0.030373102 0.027674839
## [43] 0.025216283 0.022976138 0.020935001 0.019075193 0.017380605 0.015836560
## [49] 0.014429684 0.013147791 0.011979777 0.010915527 0.009945821 0.009062262
## [55] 0.008257196 0.007523649 0.006855269 0.006246266 0.005691365 0.005185760
## [61] 0.004725071 0.004305309 0.003922837
plot(lasso.mod,"lambda", label=TRUE)

lasso.mod$lambda[5]
## [1] 0.8650368
log(lasso.mod$lambda[5])
## [1] -0.1449832
coef(lasso.mod)[,5]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##         9.59702816         0.00000000         0.00000000         0.00000000 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##         0.00000000        -0.01688762        -0.60500469         0.00000000 
##               Edad          Educacion             Urbano                USA 
##         0.00000000         0.00000000         0.00000000         0.00000000
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(lasso.mod$lambda[5]), col="blue", lwd=4, lty=3)

lasso.mod$lambda[60]
## [1] 0.00518576
log(lasso.mod$lambda[60])
## [1] -5.261839
coef(lasso.mod)[,60]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##      10.5085623004       0.0919152439       0.0155891624       0.1206150810 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##       0.0001898236      -0.0947707445      -4.8154673041      -2.8642546345 
##               Edad          Educacion             Urbano                USA 
##      -0.0457480783      -0.0191786166       0.1105346159      -0.1461768441
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(lasso.mod$lambda[60]), col="blue", lwd=4, lty=3)

datosx<-model.matrix(Ventas~.,datos)[,-c(1,2)]
pred<-predict(lasso.mod,s=lasso.mod$lambda[5],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.lasso <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 6.599002
## 
## $raiz.error.cuadratico
## [1] 2.60492
## 
## $error.relativo
## [1] 0.2751099
## 
## $correlacion
## [1] 0.5971009
pred<-predict(lasso.mod,s=lasso.mod$lambda[60],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.lasso <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 1.007691
## 
## $raiz.error.cuadratico
## [1] 1.017932
## 
## $error.relativo
## [1] 0.1071899
## 
## $correlacion
## [1] 0.9345484

El error en este caso se mide entre otros por la raiz del error cuadratico medio. Ya que lo que indica este coeficiente es que la estimacion del modelo tiene un error absoluto estimado de + (mas) o (-) 1180 unidades de asientos vendidos en el caso de regresion multiple, contra + (mas) o (-) 1047 unidades de asientos vendidos en el caso de regresion Ridge, y + (mas) o (-) 1018 unidades de asientos vendidos en el caso de regresion Lasso. El error relativo indica que el error de estimacion es aproximado de 11,46% y ademas la correlacion entre el valor real y el valor predicho es 0.9283687 en la regresion multiple. Por su parte en Ridge, el error relativo indica que el error de estimacion es aproximado de 10,96% y ademas la correlacion entre el valor real y el valor predicho es 0.9340148 en la regresion multiple.Finalmente, en Lasso, el error relativo indica que el error de estimacion es aproximado de 10,72% y ademas la correlacion entre el valor real y el valor predicho es 0.9345484 en la regresion multiple.Curiosamente esto indica que el mejor ajuste se encuentra en la regresion de Lasso, para el caso de training, cabe indicar que las diferencias no resultan significativas, por lo que puede estar asociado a la seleccion de training vs test.

  1. ¿En regresi´on Lasso, qu´e valor de λ tomar´ıa si se le pidiera obtener un modelo que no le de tanta importancia al error de predicci´on pero que sea mucho m´as f´acil de interpretar (es decir que a´un m´as coeficientes de la regresi´on sean nulos)? Genere dicho modelo, muestre los coeficientes y las medidas de error.
# Validación Cruzada
sal.cv<-cv.glmnet(x,y,alpha=1) 
plot(sal.cv)

mejor.lambda<-sal.cv$lambda.min
mejor.lambda
## [1] 0.003922837
log(mejor.lambda)
## [1] -5.54094
coef(lasso.mod)[,which(lasso.mod$lambda==mejor.lambda)]
##        (Intercept)        PrecioCompt            Ingreso         Publicidad 
##      10.5101802475       0.0921175783       0.0156407178       0.1211836561 
##          Poblacion             Precio  CalidadEstantMalo CalidadEstantMedio 
##       0.0001942996      -0.0949070828      -4.8235162298      -2.8711100334 
##               Edad          Educacion             Urbano                USA 
##      -0.0458211287      -0.0196404504       0.1135363121      -0.1550627296
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(mejor.lambda), col="blue", lwd=4, lty=3)

pred<-predict(lasso.mod,s=mejor.lambda,newx=datosx)
# Medición de precisión
numero.predictoras<- dim(datosx)[2]-1
numero.predictoras
## [1] 10
pre.lasso <- indices.precision(datos$Ventas,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 1.00744
## 
## $raiz.error.cuadratico
## [1] 1.017806
## 
## $error.relativo
## [1] 0.1071834
## 
## $correlacion
## [1] 0.9345557

El mejor lambda es de 0.003922837 y se genera en la grafica (mejor.lambda) en -5.54094.

Pregunta 3: La Tabla de Datos uscrime.csv contiene el c´alculo de´ındice de cr´ımenes violentos por habitante en Estados Unidos, como son el asesinato, la violaci´on, el robo y asalto. Las variables incluidas son, entre otras, el porcentaje de la poblaci´on considerada urbana, la renta media de la familia, la participaci´on de las fuerzas del orden, el n´umero de polic´ıas per c´apita, el porcentaje de los oficiales asignados a las unidades de la droga. La variable a predecir es ViolentCrimesPerPop (Per Capita Violent Crimes in US). Usando un 67 % de esta tabla para Tabla de Aprendizaje y el restante 33 % para Tabla de Testing efectue lo siguiente:

  1. Construya un modelo predictivo para la variable ViolentCrimesPerPop usando una Regresi´on Lineal M´ultiple con la funci´on lm(…) en la Tabla de Aprendizaje y calcule Error Est´andar de los Residuos para este modelo, adem´as calcule el Error Cuadr´atico Medio y el Error Relativo para la Tabla de Testing.
setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase8")
datos<-read.csv("uscrime.csv",dec='.',header=T)
str(datos)
## 'data.frame':    1994 obs. of  103 variables:
##  $ state                : int  8 53 24 34 42 6 44 6 21 29 ...
##  $ fold                 : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population           : num  0.19 0 0 0.04 0.01 0.02 0.01 0.01 0.03 0.01 ...
##  $ householdsize        : num  0.33 0.16 0.42 0.77 0.55 0.28 0.39 0.74 0.34 0.4 ...
##  $ racepctblack         : num  0.02 0.12 0.49 1 0.02 0.06 0 0.03 0.2 0.06 ...
##  $ racePctWhite         : num  0.9 0.74 0.56 0.08 0.95 0.54 0.98 0.46 0.84 0.87 ...
##  $ racePctAsian         : num  0.12 0.45 0.17 0.12 0.09 1 0.06 0.2 0.02 0.3 ...
##  $ racePctHisp          : num  0.17 0.07 0.04 0.1 0.05 0.25 0.02 1 0 0.03 ...
##  $ agePct12t21          : num  0.34 0.26 0.39 0.51 0.38 0.31 0.3 0.52 0.38 0.9 ...
##  $ agePct12t29          : num  0.47 0.59 0.47 0.5 0.38 0.48 0.37 0.55 0.45 0.82 ...
##  $ agePct16t24          : num  0.29 0.35 0.28 0.34 0.23 0.27 0.23 0.36 0.28 0.8 ...
##  $ agePct65up           : num  0.32 0.27 0.32 0.21 0.36 0.37 0.6 0.35 0.48 0.39 ...
##  $ numbUrban            : num  0.2 0.02 0 0.06 0.02 0.04 0.02 0 0.04 0.02 ...
##  $ pctUrban             : num  1 1 0 1 0.9 1 0.81 0 1 1 ...
##  $ medIncome            : num  0.37 0.31 0.3 0.58 0.5 0.52 0.42 0.16 0.17 0.54 ...
##  $ pctWWage             : num  0.72 0.72 0.58 0.89 0.72 0.68 0.5 0.44 0.47 0.59 ...
##  $ pctWFarmSelf         : num  0.34 0.11 0.19 0.21 0.16 0.2 0.23 1 0.36 0.22 ...
##  $ pctWInvInc           : num  0.6 0.45 0.39 0.43 0.68 0.61 0.68 0.23 0.34 0.86 ...
##  $ pctWSocSec           : num  0.29 0.25 0.38 0.36 0.44 0.28 0.61 0.53 0.55 0.42 ...
##  $ pctWPubAsst          : num  0.15 0.29 0.4 0.2 0.11 0.15 0.21 0.97 0.48 0.02 ...
##  $ pctWRetire           : num  0.43 0.39 0.84 0.82 0.71 0.25 0.54 0.41 0.43 0.31 ...
##  $ medFamInc            : num  0.39 0.29 0.28 0.51 0.46 0.62 0.43 0.15 0.21 0.85 ...
##  $ perCapInc            : num  0.4 0.37 0.27 0.36 0.43 0.72 0.47 0.1 0.23 0.89 ...
##  $ whitePerCap          : num  0.39 0.38 0.29 0.4 0.41 0.76 0.44 0.12 0.23 0.94 ...
##  $ blackPerCap          : num  0.32 0.33 0.27 0.39 0.28 0.77 0.4 0.08 0.19 0.11 ...
##  $ indianPerCap         : num  0.27 0.16 0.07 0.16 0 0.28 0.24 0.17 0.1 0.09 ...
##  $ AsianPerCap          : num  0.27 0.3 0.29 0.25 0.74 0.52 0.86 0.27 0.26 0.33 ...
##  $ OtherPerCap          : num  0.36 0.22 0.28 0.36 0.51 0.48 0.24 0.18 0.29 0.17 ...
##  $ HispPerCap           : num  0.41 0.35 0.39 0.44 0.48 0.6 0.36 0.21 0.22 0.8 ...
##  $ NumUnderPov          : num  0.08 0.01 0.01 0.01 0 0.01 0.01 0.03 0.04 0 ...
##  $ PctPopUnderPov       : num  0.19 0.24 0.27 0.1 0.06 0.12 0.11 0.64 0.45 0.11 ...
##  $ PctLess9thGrade      : num  0.1 0.14 0.27 0.09 0.25 0.13 0.29 0.96 0.52 0.04 ...
##  $ PctNotHSGrad         : num  0.18 0.24 0.43 0.25 0.3 0.12 0.41 0.82 0.59 0.03 ...
##  $ PctBSorMore          : num  0.48 0.3 0.19 0.31 0.33 0.8 0.36 0.12 0.17 1 ...
##  $ PctUnemployed        : num  0.27 0.27 0.36 0.33 0.12 0.1 0.28 1 0.55 0.11 ...
##  $ PctEmploy            : num  0.68 0.73 0.58 0.71 0.65 0.65 0.54 0.26 0.43 0.44 ...
##  $ PctEmplManu          : num  0.23 0.57 0.32 0.36 0.67 0.19 0.44 0.43 0.59 0.2 ...
##  $ PctEmplProfServ      : num  0.41 0.15 0.29 0.45 0.38 0.77 0.53 0.34 0.36 1 ...
##  $ PctOccupManu         : num  0.25 0.42 0.49 0.37 0.42 0.06 0.33 0.71 0.64 0.02 ...
##  $ PctOccupMgmtProf     : num  0.52 0.36 0.32 0.39 0.46 0.91 0.49 0.18 0.29 0.96 ...
##  $ MalePctDivorce       : num  0.68 1 0.63 0.34 0.22 0.49 0.25 0.38 0.62 0.3 ...
##  $ MalePctNevMarr       : num  0.4 0.63 0.41 0.45 0.27 0.57 0.34 0.47 0.26 0.85 ...
##  $ FemalePctDiv         : num  0.75 0.91 0.71 0.49 0.2 0.61 0.28 0.59 0.66 0.39 ...
##  $ TotalPctDiv          : num  0.75 1 0.7 0.44 0.21 0.58 0.28 0.52 0.67 0.36 ...
##  $ PersPerFam           : num  0.35 0.29 0.45 0.75 0.51 0.44 0.42 0.78 0.37 0.31 ...
##  $ PctFam2Par           : num  0.55 0.43 0.42 0.65 0.91 0.62 0.77 0.45 0.51 0.65 ...
##  $ PctKids2Par          : num  0.59 0.47 0.44 0.54 0.91 0.69 0.81 0.43 0.55 0.73 ...
##  $ PctYoungKids2Par     : num  0.61 0.6 0.43 0.83 0.89 0.87 0.79 0.34 0.58 0.78 ...
##  $ PctTeen2Par          : num  0.56 0.39 0.43 0.65 0.85 0.53 0.74 0.34 0.47 0.67 ...
##  $ PctWorkMomYoungKids  : num  0.74 0.46 0.71 0.85 0.4 0.3 0.57 0.29 0.65 0.72 ...
##  $ PctWorkMom           : num  0.76 0.53 0.67 0.86 0.6 0.43 0.62 0.27 0.64 0.71 ...
##  $ NumIlleg             : num  0.04 0 0.01 0.03 0 0 0 0.02 0.02 0 ...
##  $ PctIlleg             : num  0.14 0.24 0.46 0.33 0.06 0.11 0.13 0.5 0.29 0.07 ...
##  $ NumImmig             : num  0.03 0.01 0 0.02 0 0.04 0.01 0.02 0 0.01 ...
##  $ PctImmigRecent       : num  0.24 0.52 0.07 0.11 0.03 0.3 0 0.5 0.12 0.41 ...
##  $ PctImmigRec5         : num  0.27 0.62 0.06 0.2 0.07 0.35 0.02 0.59 0.09 0.44 ...
##  $ PctImmigRec8         : num  0.37 0.64 0.15 0.3 0.2 0.43 0.02 0.65 0.07 0.52 ...
##  $ PctImmigRec10        : num  0.39 0.63 0.19 0.31 0.27 0.47 0.1 0.59 0.13 0.48 ...
##  $ PctRecentImmig       : num  0.07 0.25 0.02 0.05 0.01 0.5 0 0.69 0 0.22 ...
##  $ PctRecImmig5         : num  0.07 0.27 0.02 0.08 0.02 0.5 0.01 0.72 0 0.21 ...
##  $ PctRecImmig8         : num  0.08 0.25 0.04 0.11 0.04 0.56 0.01 0.71 0 0.22 ...
##  $ PctRecImmig10        : num  0.08 0.23 0.05 0.11 0.05 0.57 0.03 0.6 0 0.19 ...
##  $ PctSpeakEnglOnly     : num  0.89 0.84 0.88 0.81 0.88 0.45 0.73 0.12 0.99 0.85 ...
##  $ PctNotSpeakEnglWell  : num  0.06 0.1 0.04 0.08 0.05 0.28 0.05 0.93 0.01 0.03 ...
##  $ PctLargHouseFam      : num  0.14 0.16 0.2 0.56 0.16 0.25 0.12 0.74 0.12 0.09 ...
##  $ PctLargHouseOccup    : num  0.13 0.1 0.2 0.62 0.19 0.19 0.13 0.75 0.12 0.06 ...
##  $ PersPerOccupHous     : num  0.33 0.17 0.46 0.85 0.59 0.29 0.42 0.8 0.35 0.15 ...
##  $ PersPerOwnOccHous    : num  0.39 0.29 0.52 0.77 0.6 0.53 0.54 0.68 0.38 0.34 ...
##  $ PersPerRentOccHous   : num  0.28 0.17 0.43 1 0.37 0.18 0.24 0.92 0.33 0.05 ...
##  $ PctPersOwnOccup      : num  0.55 0.26 0.42 0.94 0.89 0.39 0.65 0.39 0.5 0.48 ...
##  $ PctPersDenseHous     : num  0.09 0.2 0.15 0.12 0.02 0.26 0.03 0.89 0.1 0.03 ...
##  $ PctHousLess3BR       : num  0.51 0.82 0.51 0.01 0.19 0.73 0.46 0.66 0.64 0.58 ...
##  $ MedNumBR             : num  0.5 0 0.5 0.5 0.5 0 0.5 0 0 0 ...
##  $ HousVacant           : num  0.21 0.02 0.01 0.01 0.01 0.02 0.01 0.01 0.04 0.02 ...
##  $ PctHousOccup         : num  0.71 0.79 0.86 0.97 0.89 0.84 0.89 0.91 0.72 0.72 ...
##  $ PctHousOwnOcc        : num  0.52 0.24 0.41 0.96 0.87 0.3 0.57 0.46 0.49 0.38 ...
##  $ PctVacantBoarded     : num  0.05 0.02 0.29 0.6 0.04 0.16 0.09 0.22 0.05 0.07 ...
##  $ PctVacMore6Mos       : num  0.26 0.25 0.3 0.47 0.55 0.28 0.49 0.37 0.49 0.47 ...
##  $ MedYrHousBuilt       : num  0.65 0.65 0.52 0.52 0.73 0.25 0.38 0.6 0.5 0.04 ...
##  $ PctHousNoPhone       : num  0.14 0.16 0.47 0.11 0.05 0.02 0.05 0.28 0.57 0.01 ...
##  $ PctWOFullPlumb       : num  0.06 0 0.45 0.11 0.14 0.05 0.05 0.23 0.22 0 ...
##  $ OwnOccLowQuart       : num  0.22 0.21 0.18 0.24 0.31 0.94 0.37 0.15 0.07 0.63 ...
##  $ OwnOccMedVal         : num  0.19 0.2 0.17 0.21 0.31 1 0.38 0.13 0.07 0.71 ...
##  $ OwnOccHiQuart        : num  0.18 0.21 0.16 0.19 0.3 1 0.39 0.13 0.08 0.79 ...
##  $ RentLowQ             : num  0.36 0.42 0.27 0.75 0.4 0.67 0.26 0.21 0.14 0.44 ...
##  $ RentMedian           : num  0.35 0.38 0.29 0.7 0.36 0.63 0.35 0.24 0.17 0.42 ...
##  $ RentHighQ            : num  0.38 0.4 0.27 0.77 0.38 0.68 0.42 0.25 0.16 0.47 ...
##  $ MedRent              : num  0.34 0.37 0.31 0.89 0.38 0.62 0.35 0.24 0.15 0.41 ...
##  $ MedRentPctHousInc    : num  0.38 0.29 0.48 0.63 0.22 0.47 0.46 0.64 0.38 0.23 ...
##  $ MedOwnCostPctInc     : num  0.46 0.32 0.39 0.51 0.51 0.59 0.44 0.59 0.13 0.27 ...
##  $ MedOwnCostPctIncNoMtg: num  0.25 0.18 0.28 0.47 0.21 0.11 0.31 0.28 0.36 0.28 ...
##  $ NumInShelters        : num  0.04 0 0 0 0 0 0 0 0.01 0 ...
##  $ NumStreet            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ PctForeignBorn       : num  0.12 0.21 0.14 0.19 0.11 0.7 0.15 0.59 0.01 0.22 ...
##  $ PctBornSameState     : num  0.42 0.5 0.49 0.3 0.72 0.42 0.81 0.58 0.78 0.42 ...
##  $ PctSameHouse85       : num  0.5 0.34 0.54 0.73 0.64 0.49 0.77 0.52 0.48 0.34 ...
##  $ PctSameCity85        : num  0.51 0.6 0.67 0.64 0.61 0.73 0.91 0.79 0.79 0.23 ...
##  $ PctSameState85       : num  0.64 0.52 0.56 0.65 0.53 0.64 0.84 0.78 0.75 0.09 ...
##  $ LandArea             : num  0.12 0.02 0.01 0.02 0.04 0.01 0.05 0.01 0.04 0 ...
##   [list output truncated]
suppressMessages(suppressWarnings(library(FactoMineR)))
suppressMessages(suppressWarnings(library(car)))
Atipicos<-(Boxplot(~ViolentCrimesPerPop, data=datos, id.method="y",col="Blue")) 

library(corrplot)
matriz.correlacion<-cor(datos)
corrplot(matriz.correlacion)

muestra <- sample(1:nrow(datos),floor(nrow(datos)*0.33))
ttesting <- datos[muestra,]
taprendizaje <- datos[-muestra,]
nrow(ttesting)
## [1] 658
nrow(taprendizaje)
## [1] 1336
modelo <- lm(ViolentCrimesPerPop ~ ., data = datos)
modelo 
## 
## Call:
## lm(formula = ViolentCrimesPerPop ~ ., data = datos)
## 
## Coefficients:
##           (Intercept)                  state                   fold  
##             0.5987695             -0.0007437             -0.0015748  
##            population          householdsize           racepctblack  
##             0.1321909              0.0007522              0.2000846  
##          racePctWhite           racePctAsian            racePctHisp  
##            -0.0545393             -0.0132278              0.0537790  
##           agePct12t21            agePct12t29            agePct16t24  
##             0.1156000             -0.2374790             -0.1330132  
##            agePct65up              numbUrban               pctUrban  
##             0.0364240             -0.2461314              0.0468133  
##             medIncome               pctWWage           pctWFarmSelf  
##            -0.1778505             -0.1977079              0.0466592  
##            pctWInvInc             pctWSocSec            pctWPubAsst  
##            -0.1600134              0.0829735             -0.0064111  
##            pctWRetire              medFamInc              perCapInc  
##            -0.0862223              0.2783261              0.1090832  
##           whitePerCap            blackPerCap           indianPerCap  
##            -0.3540081             -0.0322052             -0.0332266  
##           AsianPerCap            OtherPerCap             HispPerCap  
##             0.0198696              0.0446077              0.0312484  
##           NumUnderPov         PctPopUnderPov        PctLess9thGrade  
##             0.1257068             -0.1723615             -0.1019432  
##          PctNotHSGrad            PctBSorMore          PctUnemployed  
##             0.0529294              0.0548989              0.0024203  
##             PctEmploy            PctEmplManu        PctEmplProfServ  
##             0.2636937             -0.0611585             -0.0231200  
##          PctOccupManu       PctOccupMgmtProf         MalePctDivorce  
##             0.0725969              0.1095694              0.4315918  
##        MalePctNevMarr           FemalePctDiv            TotalPctDiv  
##             0.2211650              0.1139415             -0.4977383  
##            PersPerFam             PctFam2Par            PctKids2Par  
##            -0.1600699             -0.0143193             -0.2870569  
##      PctYoungKids2Par            PctTeen2Par    PctWorkMomYoungKids  
##            -0.0267124             -0.0025639              0.0523100  
##            PctWorkMom               NumIlleg               PctIlleg  
##            -0.1888670             -0.1383371              0.1143173  
##              NumImmig         PctImmigRecent           PctImmigRec5  
##            -0.1403383              0.0221218              0.0242018  
##          PctImmigRec8          PctImmigRec10         PctRecentImmig  
##            -0.0690847              0.0360765             -0.0244706  
##          PctRecImmig5           PctRecImmig8          PctRecImmig10  
##            -0.2037796              0.3916334             -0.1607644  
##      PctSpeakEnglOnly    PctNotSpeakEnglWell        PctLargHouseFam  
##            -0.0265885             -0.1367050              0.0572615  
##     PctLargHouseOccup       PersPerOccupHous      PersPerOwnOccHous  
##            -0.1874715              0.5663797             -0.0452153  
##    PersPerRentOccHous        PctPersOwnOccup       PctPersDenseHous  
##            -0.2410337             -0.6954982              0.2086600  
##        PctHousLess3BR               MedNumBR             HousVacant  
##             0.0849002              0.0265379              0.1541798  
##          PctHousOccup          PctHousOwnOcc       PctVacantBoarded  
##            -0.0495127              0.5636854              0.0543938  
##        PctVacMore6Mos         MedYrHousBuilt         PctHousNoPhone  
##            -0.0717602             -0.0231802              0.0189781  
##        PctWOFullPlumb         OwnOccLowQuart           OwnOccMedVal  
##            -0.0139302             -0.3956506              0.2677203  
##         OwnOccHiQuart               RentLowQ             RentMedian  
##             0.0194530             -0.2313899             -0.0012953  
##             RentHighQ                MedRent      MedRentPctHousInc  
##            -0.0571824              0.3417753              0.0424070  
##      MedOwnCostPctInc  MedOwnCostPctIncNoMtg          NumInShelters  
##            -0.0404555             -0.0739525              0.1343450  
##             NumStreet         PctForeignBorn       PctBornSameState  
##             0.1754496              0.1145705              0.0166479  
##        PctSameHouse85          PctSameCity85         PctSameState85  
##            -0.0038044              0.0190145              0.0134813  
##              LandArea                PopDens         PctUsePubTrans  
##             0.0176208             -0.0113547             -0.0370163  
##   LemasPctOfficDrugUn  
##             0.0244668
summary(modelo)
## 
## Call:
## lm(formula = ViolentCrimesPerPop ~ ., data = datos)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.49636 -0.07250 -0.01369  0.05088  0.74425 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.5987695  0.2031083   2.948 0.003237 ** 
## state                 -0.0007437  0.0002498  -2.977 0.002948 ** 
## fold                  -0.0015748  0.0010543  -1.494 0.135420    
## population             0.1321909  0.3968189   0.333 0.739076    
## householdsize          0.0007522  0.0865043   0.009 0.993063    
## racepctblack           0.2000846  0.0510870   3.917  9.3e-05 ***
## racePctWhite          -0.0545393  0.0587189  -0.929 0.353102    
## racePctAsian          -0.0132278  0.0343102  -0.386 0.699885    
## racePctHisp            0.0537790  0.0533900   1.007 0.313926    
## agePct12t21            0.1156000  0.1057256   1.093 0.274359    
## agePct12t29           -0.2374790  0.1562370  -1.520 0.128680    
## agePct16t24           -0.1330132  0.1639809  -0.811 0.417381    
## agePct65up             0.0364240  0.1033839   0.352 0.724639    
## numbUrban             -0.2461314  0.3866974  -0.636 0.524530    
## pctUrban               0.0468133  0.0156113   2.999 0.002747 ** 
## medIncome             -0.1778505  0.1724501  -1.031 0.302525    
## pctWWage              -0.1977079  0.0894399  -2.211 0.027189 *  
## pctWFarmSelf           0.0466592  0.0201155   2.320 0.020470 *  
## pctWInvInc            -0.1600134  0.0677020  -2.363 0.018204 *  
## pctWSocSec             0.0829735  0.1069258   0.776 0.437851    
## pctWPubAsst           -0.0064111  0.0461302  -0.139 0.889482    
## pctWRetire            -0.0862223  0.0367757  -2.345 0.019153 *  
## medFamInc              0.2783261  0.1601537   1.738 0.082397 .  
## perCapInc              0.1090832  0.1884590   0.579 0.562782    
## whitePerCap           -0.3540081  0.1522613  -2.325 0.020177 *  
## blackPerCap           -0.0322052  0.0254324  -1.266 0.205560    
## indianPerCap          -0.0332266  0.0193899  -1.714 0.086766 .  
## AsianPerCap            0.0198696  0.0189010   1.051 0.293279    
## OtherPerCap            0.0446077  0.0186864   2.387 0.017076 *  
## HispPerCap             0.0312484  0.0248313   1.258 0.208394    
## NumUnderPov            0.1257068  0.1378545   0.912 0.361948    
## PctPopUnderPov        -0.1723615  0.0626955  -2.749 0.006031 ** 
## PctLess9thGrade       -0.1019432  0.0677375  -1.505 0.132497    
## PctNotHSGrad           0.0529294  0.0957927   0.553 0.580643    
## PctBSorMore            0.0548989  0.0773171   0.710 0.477762    
## PctUnemployed          0.0024203  0.0406962   0.059 0.952583    
## PctEmploy              0.2636937  0.0789289   3.341 0.000851 ***
## PctEmplManu           -0.0611585  0.0320386  -1.909 0.056426 .  
## PctEmplProfServ       -0.0231200  0.0407967  -0.567 0.570976    
## PctOccupManu           0.0725969  0.0549134   1.322 0.186320    
## PctOccupMgmtProf       0.1095694  0.0862375   1.271 0.204043    
## MalePctDivorce         0.4315918  0.2473789   1.745 0.081207 .  
## MalePctNevMarr         0.2211650  0.0678878   3.258 0.001143 ** 
## FemalePctDiv           0.1139415  0.3091690   0.369 0.712511    
## TotalPctDiv           -0.4977383  0.5179046  -0.961 0.336644    
## PersPerFam            -0.1600699  0.1683328  -0.951 0.341770    
## PctFam2Par            -0.0143193  0.1597137  -0.090 0.928570    
## PctKids2Par           -0.2870569  0.1555362  -1.846 0.065107 .  
## PctYoungKids2Par      -0.0267124  0.0481925  -0.554 0.579449    
## PctTeen2Par           -0.0025639  0.0425832  -0.060 0.951996    
## PctWorkMomYoungKids    0.0523100  0.0469896   1.113 0.265753    
## PctWorkMom            -0.1888670  0.0537661  -3.513 0.000454 ***
## NumIlleg              -0.1383371  0.1083610  -1.277 0.201889    
## PctIlleg               0.1143173  0.0474663   2.408 0.016118 *  
## NumImmig              -0.1403383  0.0778895  -1.802 0.071742 .  
## PctImmigRecent         0.0221218  0.0410039   0.540 0.589603    
## PctImmigRec5           0.0242018  0.0664942   0.364 0.715922    
## PctImmigRec8          -0.0690847  0.0770920  -0.896 0.370296    
## PctImmigRec10          0.0360765  0.0595959   0.605 0.545018    
## PctRecentImmig        -0.0244706  0.1220592  -0.200 0.841126    
## PctRecImmig5          -0.2037796  0.2211229  -0.922 0.356872    
## PctRecImmig8           0.3916334  0.2731670   1.434 0.151830    
## PctRecImmig10         -0.1607644  0.2188420  -0.735 0.462666    
## PctSpeakEnglOnly      -0.0265885  0.0702968  -0.378 0.705301    
## PctNotSpeakEnglWell   -0.1367050  0.0684070  -1.998 0.045816 *  
## PctLargHouseFam        0.0572615  0.2258332   0.254 0.799866    
## PctLargHouseOccup     -0.1874715  0.2363916  -0.793 0.427845    
## PersPerOccupHous       0.5663797  0.2503123   2.263 0.023768 *  
## PersPerOwnOccHous     -0.0452153  0.1677007  -0.270 0.787483    
## PersPerRentOccHous    -0.2410337  0.0808856  -2.980 0.002920 ** 
## PctPersOwnOccup       -0.6954982  0.3576874  -1.944 0.051992 .  
## PctPersDenseHous       0.2086600  0.0755707   2.761 0.005816 ** 
## PctHousLess3BR         0.0849002  0.0588404   1.443 0.149217    
## MedNumBR               0.0265379  0.0194323   1.366 0.172207    
## HousVacant             0.1541798  0.0729662   2.113 0.034729 *  
## PctHousOccup          -0.0495127  0.0309284  -1.601 0.109570    
## PctHousOwnOcc          0.5636854  0.3740216   1.507 0.131954    
## PctVacantBoarded       0.0543938  0.0214006   2.542 0.011111 *  
## PctVacMore6Mos        -0.0717602  0.0251453  -2.854 0.004367 ** 
## MedYrHousBuilt        -0.0231802  0.0289186  -0.802 0.422904    
## PctHousNoPhone         0.0189781  0.0352531   0.538 0.590407    
## PctWOFullPlumb        -0.0139302  0.0202350  -0.688 0.491271    
## OwnOccLowQuart        -0.3956506  0.2044847  -1.935 0.053156 .  
## OwnOccMedVal           0.2677203  0.3069081   0.872 0.383148    
## OwnOccHiQuart          0.0194530  0.1645854   0.118 0.905927    
## RentLowQ              -0.2313899  0.0669777  -3.455 0.000563 ***
## RentMedian            -0.0012953  0.1565332  -0.008 0.993399    
## RentHighQ             -0.0571824  0.0861307  -0.664 0.506833    
## MedRent                0.3417753  0.1296496   2.636 0.008454 ** 
## MedRentPctHousInc      0.0424070  0.0325435   1.303 0.192703    
## MedOwnCostPctInc      -0.0404555  0.0344644  -1.174 0.240609    
## MedOwnCostPctIncNoMtg -0.0739525  0.0246266  -3.003 0.002709 ** 
## NumInShelters          0.1343450  0.0641192   2.095 0.036282 *  
## NumStreet              0.1754496  0.0470896   3.726 0.000200 ***
## PctForeignBorn         0.1145705  0.0898118   1.276 0.202228    
## PctBornSameState       0.0166479  0.0417179   0.399 0.689895    
## PctSameHouse85        -0.0038044  0.0577602  -0.066 0.947492    
## PctSameCity85          0.0190145  0.0381035   0.499 0.617821    
## PctSameState85         0.0134813  0.0426348   0.316 0.751883    
## LandArea               0.0176208  0.0490691   0.359 0.719559    
## PopDens               -0.0113547  0.0303202  -0.374 0.708081    
## PctUsePubTrans        -0.0370163  0.0231283  -1.600 0.109661    
## LemasPctOfficDrugUn    0.0244668  0.0154392   1.585 0.113197    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1318 on 1891 degrees of freedom
## Multiple R-squared:  0.6965, Adjusted R-squared:  0.6801 
## F-statistic: 42.54 on 102 and 1891 DF,  p-value: < 2.2e-16
# Calcula el modelo usando solo los datos de training
modelo.lm <- lm(ViolentCrimesPerPop~., data = taprendizaje)
modelo.lm
## 
## Call:
## lm(formula = ViolentCrimesPerPop ~ ., data = taprendizaje)
## 
## Coefficients:
##           (Intercept)                  state                   fold  
##             0.7301321             -0.0005933             -0.0017368  
##            population          householdsize           racepctblack  
##            -0.0269510              0.1004843              0.1236990  
##          racePctWhite           racePctAsian            racePctHisp  
##            -0.1437003             -0.0136794              0.0632988  
##           agePct12t21            agePct12t29            agePct16t24  
##             0.1643552             -0.1815100             -0.1828198  
##            agePct65up              numbUrban               pctUrban  
##             0.0195642             -0.1106344              0.0441169  
##             medIncome               pctWWage           pctWFarmSelf  
##            -0.2885981             -0.1594796              0.0302056  
##            pctWInvInc             pctWSocSec            pctWPubAsst  
##            -0.1186551              0.0885469             -0.0189197  
##            pctWRetire              medFamInc              perCapInc  
##            -0.0795092              0.3339612              0.1833208  
##           whitePerCap            blackPerCap           indianPerCap  
##            -0.4143148             -0.0432160             -0.0329392  
##           AsianPerCap            OtherPerCap             HispPerCap  
##             0.0050798              0.0291808              0.0579221  
##           NumUnderPov         PctPopUnderPov        PctLess9thGrade  
##             0.1147659             -0.1817281             -0.0572288  
##          PctNotHSGrad            PctBSorMore          PctUnemployed  
##             0.0424882              0.0315698              0.0096606  
##             PctEmploy            PctEmplManu        PctEmplProfServ  
##             0.2326172             -0.0813150             -0.0113773  
##          PctOccupManu       PctOccupMgmtProf         MalePctDivorce  
##             0.0817852              0.0899506              0.3950949  
##        MalePctNevMarr           FemalePctDiv            TotalPctDiv  
##             0.0998573              0.0525902             -0.4152243  
##            PersPerFam             PctFam2Par            PctKids2Par  
##            -0.2326905             -0.1843596             -0.1693039  
##      PctYoungKids2Par            PctTeen2Par    PctWorkMomYoungKids  
##            -0.0130657             -0.0151195              0.0737985  
##            PctWorkMom               NumIlleg               PctIlleg  
##            -0.2160931             -0.2395280              0.1607720  
##              NumImmig         PctImmigRecent           PctImmigRec5  
##            -0.1207343              0.0516954              0.0117994  
##          PctImmigRec8          PctImmigRec10         PctRecentImmig  
##            -0.0904042              0.0762501             -0.0206852  
##          PctRecImmig5           PctRecImmig8          PctRecImmig10  
##            -0.4302941              0.5926712             -0.0721819  
##      PctSpeakEnglOnly    PctNotSpeakEnglWell        PctLargHouseFam  
##            -0.0643134             -0.1564434              0.1642796  
##     PctLargHouseOccup       PersPerOccupHous      PersPerOwnOccHous  
##            -0.2961117              0.5282477              0.0516878  
##    PersPerRentOccHous        PctPersOwnOccup       PctPersDenseHous  
##            -0.3074837             -0.9441568              0.1575127  
##        PctHousLess3BR               MedNumBR             HousVacant  
##             0.0887776              0.0264781              0.2230062  
##          PctHousOccup          PctHousOwnOcc       PctVacantBoarded  
##            -0.0416508              0.7927967              0.0336534  
##        PctVacMore6Mos         MedYrHousBuilt         PctHousNoPhone  
##            -0.0716178             -0.0122728             -0.0202421  
##        PctWOFullPlumb         OwnOccLowQuart           OwnOccMedVal  
##            -0.0210938             -0.1794044              0.0972415  
##         OwnOccHiQuart               RentLowQ             RentMedian  
##             0.0467856             -0.2278201             -0.1073302  
##             RentHighQ                MedRent      MedRentPctHousInc  
##             0.0083983              0.3802668              0.0431364  
##      MedOwnCostPctInc  MedOwnCostPctIncNoMtg          NumInShelters  
##            -0.0552143             -0.0512172              0.2211884  
##             NumStreet         PctForeignBorn       PctBornSameState  
##             0.1516897              0.0156050             -0.0006032  
##        PctSameHouse85          PctSameCity85         PctSameState85  
##             0.0454816              0.0269999              0.0097497  
##              LandArea                PopDens         PctUsePubTrans  
##             0.0064374             -0.0441963             -0.0510265  
##   LemasPctOfficDrugUn  
##             0.0251963
numero.predictoras <- dim(datos)[2] - 1
# Hace la Predicción
prediccion <- predict(modelo.lm, ttesting)
# Medición de precisión
pre.lm <- indices.precision(ttesting$ViolentCrimesPerPop, prediccion,numero.predictoras)
pre.lm
## $error.cuadratico
## [1] 0.01895867
## 
## $raiz.error.cuadratico
## [1] 0.1499237
## 
## $error.relativo
## [1] 0.4038489
## 
## $correlacion
## [1] 0.8265094
# Gráfico real vs predicción, con curva de mejor ajuste lineal
library(ggplot2)
g <- plot.real.prediccion(ttesting$ViolentCrimesPerPop, prediccion, modelo = "Regresión Lineal")
g + geom_smooth(method = lm, size = 0.4, color = "red", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'

prediccion <- predict(modelo,datos, interval="confidence")
hist(prediccion, col = "green")

###Ridge

# La siguiente instrucción construye una matriz con los predictores
x<-model.matrix(ViolentCrimesPerPop~.,datos)
head(x)
##   (Intercept) state fold population householdsize racepctblack racePctWhite
## 1           1     8    1       0.19          0.33         0.02         0.90
## 2           1    53    1       0.00          0.16         0.12         0.74
## 3           1    24    1       0.00          0.42         0.49         0.56
## 4           1    34    1       0.04          0.77         1.00         0.08
## 5           1    42    1       0.01          0.55         0.02         0.95
## 6           1     6    1       0.02          0.28         0.06         0.54
##   racePctAsian racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up
## 1         0.12        0.17        0.34        0.47        0.29       0.32
## 2         0.45        0.07        0.26        0.59        0.35       0.27
## 3         0.17        0.04        0.39        0.47        0.28       0.32
## 4         0.12        0.10        0.51        0.50        0.34       0.21
## 5         0.09        0.05        0.38        0.38        0.23       0.36
## 6         1.00        0.25        0.31        0.48        0.27       0.37
##   numbUrban pctUrban medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec
## 1      0.20      1.0      0.37     0.72         0.34       0.60       0.29
## 2      0.02      1.0      0.31     0.72         0.11       0.45       0.25
## 3      0.00      0.0      0.30     0.58         0.19       0.39       0.38
## 4      0.06      1.0      0.58     0.89         0.21       0.43       0.36
## 5      0.02      0.9      0.50     0.72         0.16       0.68       0.44
## 6      0.04      1.0      0.52     0.68         0.20       0.61       0.28
##   pctWPubAsst pctWRetire medFamInc perCapInc whitePerCap blackPerCap
## 1        0.15       0.43      0.39      0.40        0.39        0.32
## 2        0.29       0.39      0.29      0.37        0.38        0.33
## 3        0.40       0.84      0.28      0.27        0.29        0.27
## 4        0.20       0.82      0.51      0.36        0.40        0.39
## 5        0.11       0.71      0.46      0.43        0.41        0.28
## 6        0.15       0.25      0.62      0.72        0.76        0.77
##   indianPerCap AsianPerCap OtherPerCap HispPerCap NumUnderPov PctPopUnderPov
## 1         0.27        0.27        0.36       0.41        0.08           0.19
## 2         0.16        0.30        0.22       0.35        0.01           0.24
## 3         0.07        0.29        0.28       0.39        0.01           0.27
## 4         0.16        0.25        0.36       0.44        0.01           0.10
## 5         0.00        0.74        0.51       0.48        0.00           0.06
## 6         0.28        0.52        0.48       0.60        0.01           0.12
##   PctLess9thGrade PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu
## 1            0.10         0.18        0.48          0.27      0.68        0.23
## 2            0.14         0.24        0.30          0.27      0.73        0.57
## 3            0.27         0.43        0.19          0.36      0.58        0.32
## 4            0.09         0.25        0.31          0.33      0.71        0.36
## 5            0.25         0.30        0.33          0.12      0.65        0.67
## 6            0.13         0.12        0.80          0.10      0.65        0.19
##   PctEmplProfServ PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr
## 1            0.41         0.25             0.52           0.68           0.40
## 2            0.15         0.42             0.36           1.00           0.63
## 3            0.29         0.49             0.32           0.63           0.41
## 4            0.45         0.37             0.39           0.34           0.45
## 5            0.38         0.42             0.46           0.22           0.27
## 6            0.77         0.06             0.91           0.49           0.57
##   FemalePctDiv TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par
## 1         0.75        0.75       0.35       0.55        0.59             0.61
## 2         0.91        1.00       0.29       0.43        0.47             0.60
## 3         0.71        0.70       0.45       0.42        0.44             0.43
## 4         0.49        0.44       0.75       0.65        0.54             0.83
## 5         0.20        0.21       0.51       0.91        0.91             0.89
## 6         0.61        0.58       0.44       0.62        0.69             0.87
##   PctTeen2Par PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig
## 1        0.56                0.74       0.76     0.04     0.14     0.03
## 2        0.39                0.46       0.53     0.00     0.24     0.01
## 3        0.43                0.71       0.67     0.01     0.46     0.00
## 4        0.65                0.85       0.86     0.03     0.33     0.02
## 5        0.85                0.40       0.60     0.00     0.06     0.00
## 6        0.53                0.30       0.43     0.00     0.11     0.04
##   PctImmigRecent PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig
## 1           0.24         0.27         0.37          0.39           0.07
## 2           0.52         0.62         0.64          0.63           0.25
## 3           0.07         0.06         0.15          0.19           0.02
## 4           0.11         0.20         0.30          0.31           0.05
## 5           0.03         0.07         0.20          0.27           0.01
## 6           0.30         0.35         0.43          0.47           0.50
##   PctRecImmig5 PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## 1         0.07         0.08          0.08             0.89                0.06
## 2         0.27         0.25          0.23             0.84                0.10
## 3         0.02         0.04          0.05             0.88                0.04
## 4         0.08         0.11          0.11             0.81                0.08
## 5         0.02         0.04          0.05             0.88                0.05
## 6         0.50         0.56          0.57             0.45                0.28
##   PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## 1            0.14              0.13             0.33              0.39
## 2            0.16              0.10             0.17              0.29
## 3            0.20              0.20             0.46              0.52
## 4            0.56              0.62             0.85              0.77
## 5            0.16              0.19             0.59              0.60
## 6            0.25              0.19             0.29              0.53
##   PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
## 1               0.28            0.55             0.09           0.51      0.5
## 2               0.17            0.26             0.20           0.82      0.0
## 3               0.43            0.42             0.15           0.51      0.5
## 4               1.00            0.94             0.12           0.01      0.5
## 5               0.37            0.89             0.02           0.19      0.5
## 6               0.18            0.39             0.26           0.73      0.0
##   HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
## 1       0.21         0.71          0.52             0.05           0.26
## 2       0.02         0.79          0.24             0.02           0.25
## 3       0.01         0.86          0.41             0.29           0.30
## 4       0.01         0.97          0.96             0.60           0.47
## 5       0.01         0.89          0.87             0.04           0.55
## 6       0.02         0.84          0.30             0.16           0.28
##   MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
## 1           0.65           0.14           0.06           0.22         0.19
## 2           0.65           0.16           0.00           0.21         0.20
## 3           0.52           0.47           0.45           0.18         0.17
## 4           0.52           0.11           0.11           0.24         0.21
## 5           0.73           0.05           0.14           0.31         0.31
## 6           0.25           0.02           0.05           0.94         1.00
##   OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
## 1          0.18     0.36       0.35      0.38    0.34              0.38
## 2          0.21     0.42       0.38      0.40    0.37              0.29
## 3          0.16     0.27       0.29      0.27    0.31              0.48
## 4          0.19     0.75       0.70      0.77    0.89              0.63
## 5          0.30     0.40       0.36      0.38    0.38              0.22
## 6          1.00     0.67       0.63      0.68    0.62              0.47
##   MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
## 1             0.46                  0.25          0.04         0           0.12
## 2             0.32                  0.18          0.00         0           0.21
## 3             0.39                  0.28          0.00         0           0.14
## 4             0.51                  0.47          0.00         0           0.19
## 5             0.51                  0.21          0.00         0           0.11
## 6             0.59                  0.11          0.00         0           0.70
##   PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
## 1             0.42           0.50          0.51           0.64     0.12    0.26
## 2             0.50           0.34          0.60           0.52     0.02    0.12
## 3             0.49           0.54          0.67           0.56     0.01    0.21
## 4             0.30           0.73          0.64           0.65     0.02    0.39
## 5             0.72           0.64          0.61           0.53     0.04    0.09
## 6             0.42           0.49          0.73           0.64     0.01    0.58
##   PctUsePubTrans LemasPctOfficDrugUn
## 1           0.20                0.32
## 2           0.45                0.00
## 3           0.02                0.00
## 4           0.28                0.00
## 5           0.02                0.00
## 6           0.10                0.00
# Debemos eliminar la columna 1
x<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]
head(x)
##   (Intercept) state fold population householdsize racepctblack racePctWhite
## 1           1     8    1       0.19          0.33         0.02         0.90
## 2           1    53    1       0.00          0.16         0.12         0.74
## 3           1    24    1       0.00          0.42         0.49         0.56
## 4           1    34    1       0.04          0.77         1.00         0.08
## 5           1    42    1       0.01          0.55         0.02         0.95
## 6           1     6    1       0.02          0.28         0.06         0.54
##   racePctAsian racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up
## 1         0.12        0.17        0.34        0.47        0.29       0.32
## 2         0.45        0.07        0.26        0.59        0.35       0.27
## 3         0.17        0.04        0.39        0.47        0.28       0.32
## 4         0.12        0.10        0.51        0.50        0.34       0.21
## 5         0.09        0.05        0.38        0.38        0.23       0.36
## 6         1.00        0.25        0.31        0.48        0.27       0.37
##   numbUrban pctUrban medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec
## 1      0.20      1.0      0.37     0.72         0.34       0.60       0.29
## 2      0.02      1.0      0.31     0.72         0.11       0.45       0.25
## 3      0.00      0.0      0.30     0.58         0.19       0.39       0.38
## 4      0.06      1.0      0.58     0.89         0.21       0.43       0.36
## 5      0.02      0.9      0.50     0.72         0.16       0.68       0.44
## 6      0.04      1.0      0.52     0.68         0.20       0.61       0.28
##   pctWPubAsst pctWRetire medFamInc perCapInc whitePerCap blackPerCap
## 1        0.15       0.43      0.39      0.40        0.39        0.32
## 2        0.29       0.39      0.29      0.37        0.38        0.33
## 3        0.40       0.84      0.28      0.27        0.29        0.27
## 4        0.20       0.82      0.51      0.36        0.40        0.39
## 5        0.11       0.71      0.46      0.43        0.41        0.28
## 6        0.15       0.25      0.62      0.72        0.76        0.77
##   indianPerCap AsianPerCap OtherPerCap HispPerCap NumUnderPov PctPopUnderPov
## 1         0.27        0.27        0.36       0.41        0.08           0.19
## 2         0.16        0.30        0.22       0.35        0.01           0.24
## 3         0.07        0.29        0.28       0.39        0.01           0.27
## 4         0.16        0.25        0.36       0.44        0.01           0.10
## 5         0.00        0.74        0.51       0.48        0.00           0.06
## 6         0.28        0.52        0.48       0.60        0.01           0.12
##   PctLess9thGrade PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu
## 1            0.10         0.18        0.48          0.27      0.68        0.23
## 2            0.14         0.24        0.30          0.27      0.73        0.57
## 3            0.27         0.43        0.19          0.36      0.58        0.32
## 4            0.09         0.25        0.31          0.33      0.71        0.36
## 5            0.25         0.30        0.33          0.12      0.65        0.67
## 6            0.13         0.12        0.80          0.10      0.65        0.19
##   PctEmplProfServ PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr
## 1            0.41         0.25             0.52           0.68           0.40
## 2            0.15         0.42             0.36           1.00           0.63
## 3            0.29         0.49             0.32           0.63           0.41
## 4            0.45         0.37             0.39           0.34           0.45
## 5            0.38         0.42             0.46           0.22           0.27
## 6            0.77         0.06             0.91           0.49           0.57
##   FemalePctDiv TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par
## 1         0.75        0.75       0.35       0.55        0.59             0.61
## 2         0.91        1.00       0.29       0.43        0.47             0.60
## 3         0.71        0.70       0.45       0.42        0.44             0.43
## 4         0.49        0.44       0.75       0.65        0.54             0.83
## 5         0.20        0.21       0.51       0.91        0.91             0.89
## 6         0.61        0.58       0.44       0.62        0.69             0.87
##   PctTeen2Par PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig
## 1        0.56                0.74       0.76     0.04     0.14     0.03
## 2        0.39                0.46       0.53     0.00     0.24     0.01
## 3        0.43                0.71       0.67     0.01     0.46     0.00
## 4        0.65                0.85       0.86     0.03     0.33     0.02
## 5        0.85                0.40       0.60     0.00     0.06     0.00
## 6        0.53                0.30       0.43     0.00     0.11     0.04
##   PctImmigRecent PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig
## 1           0.24         0.27         0.37          0.39           0.07
## 2           0.52         0.62         0.64          0.63           0.25
## 3           0.07         0.06         0.15          0.19           0.02
## 4           0.11         0.20         0.30          0.31           0.05
## 5           0.03         0.07         0.20          0.27           0.01
## 6           0.30         0.35         0.43          0.47           0.50
##   PctRecImmig5 PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## 1         0.07         0.08          0.08             0.89                0.06
## 2         0.27         0.25          0.23             0.84                0.10
## 3         0.02         0.04          0.05             0.88                0.04
## 4         0.08         0.11          0.11             0.81                0.08
## 5         0.02         0.04          0.05             0.88                0.05
## 6         0.50         0.56          0.57             0.45                0.28
##   PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## 1            0.14              0.13             0.33              0.39
## 2            0.16              0.10             0.17              0.29
## 3            0.20              0.20             0.46              0.52
## 4            0.56              0.62             0.85              0.77
## 5            0.16              0.19             0.59              0.60
## 6            0.25              0.19             0.29              0.53
##   PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
## 1               0.28            0.55             0.09           0.51      0.5
## 2               0.17            0.26             0.20           0.82      0.0
## 3               0.43            0.42             0.15           0.51      0.5
## 4               1.00            0.94             0.12           0.01      0.5
## 5               0.37            0.89             0.02           0.19      0.5
## 6               0.18            0.39             0.26           0.73      0.0
##   HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
## 1       0.21         0.71          0.52             0.05           0.26
## 2       0.02         0.79          0.24             0.02           0.25
## 3       0.01         0.86          0.41             0.29           0.30
## 4       0.01         0.97          0.96             0.60           0.47
## 5       0.01         0.89          0.87             0.04           0.55
## 6       0.02         0.84          0.30             0.16           0.28
##   MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
## 1           0.65           0.14           0.06           0.22         0.19
## 2           0.65           0.16           0.00           0.21         0.20
## 3           0.52           0.47           0.45           0.18         0.17
## 4           0.52           0.11           0.11           0.24         0.21
## 5           0.73           0.05           0.14           0.31         0.31
## 6           0.25           0.02           0.05           0.94         1.00
##   OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
## 1          0.18     0.36       0.35      0.38    0.34              0.38
## 2          0.21     0.42       0.38      0.40    0.37              0.29
## 3          0.16     0.27       0.29      0.27    0.31              0.48
## 4          0.19     0.75       0.70      0.77    0.89              0.63
## 5          0.30     0.40       0.36      0.38    0.38              0.22
## 6          1.00     0.67       0.63      0.68    0.62              0.47
##   MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
## 1             0.46                  0.25          0.04         0           0.12
## 2             0.32                  0.18          0.00         0           0.21
## 3             0.39                  0.28          0.00         0           0.14
## 4             0.51                  0.47          0.00         0           0.19
## 5             0.51                  0.21          0.00         0           0.11
## 6             0.59                  0.11          0.00         0           0.70
##   PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
## 1             0.42           0.50          0.51           0.64     0.12    0.26
## 2             0.50           0.34          0.60           0.52     0.02    0.12
## 3             0.49           0.54          0.67           0.56     0.01    0.21
## 4             0.30           0.73          0.64           0.65     0.02    0.39
## 5             0.72           0.64          0.61           0.53     0.04    0.09
## 6             0.42           0.49          0.73           0.64     0.01    0.58
##   PctUsePubTrans
## 1           0.20
## 2           0.45
## 3           0.02
## 4           0.28
## 5           0.02
## 6           0.10
# La siguiente instrucción construye la variable a predecir
y<-datos$ViolentCrimesPerPop
library(glmnet)
ridge.mod<-glmnet(x,y,alpha=0)
dim(coef(ridge.mod))
## [1] 103 100
coef(ridge.mod)
## 103 x 100 sparse Matrix of class "dgCMatrix"
##    [[ suppressing 100 column names 's0', 's1', 's2' ... ]]
##                                                                              
## (Intercept)            2.379789e-01  2.401887e-01  2.403942e-01  2.406177e-01
## (Intercept)            .             .             .             .           
## state                 -3.033985e-39 -4.411398e-06 -4.835893e-06 -5.300705e-06
## fold                  -2.667833e-39 -3.831321e-06 -4.194991e-06 -4.592408e-06
## population             6.808685e-37  9.746098e-04  1.066772e-03  1.167395e-03
## householdsize         -5.020027e-38 -7.384222e-05 -8.104090e-05 -8.893632e-05
## racepctblack           5.861715e-37  8.434881e-04  9.237309e-04  1.011439e-03
## racePctWhite          -6.603544e-37 -9.472854e-04 -1.037089e-03 -1.135185e-03
## racePctAsian           4.238776e-38  6.324903e-05  6.950474e-05  7.637546e-05
## racePctHisp            2.966384e-37  4.200841e-04  4.593238e-04  5.020800e-04
## agePct12t21            9.170750e-38  1.215352e-04  1.319847e-04  1.432130e-04
## agePct12t29            2.513919e-37  3.483904e-04  3.801112e-04  4.145254e-04
## agePct16t24            1.404170e-37  1.918786e-04  2.090512e-04  2.276248e-04
## agePct65up             8.822151e-38  1.257556e-04  1.375868e-04  1.505019e-04
## numbUrban              6.658835e-37  9.543828e-04  1.044773e-03  1.143476e-03
## pctUrban               4.339763e-38  6.582533e-05  7.244832e-05  7.974827e-05
## medIncome             -4.768540e-37 -6.714527e-04 -7.337628e-04 -8.015900e-04
## pctWWage              -3.930613e-37 -5.574936e-04 -6.096669e-04 -6.665394e-04
## pctWFarmSelf          -1.765541e-37 -2.562046e-04 -2.808054e-04 -3.077312e-04
## pctWInvInc            -7.616699e-37 -1.082114e-03 -1.183586e-03 -1.294225e-03
## pctWSocSec             1.599169e-37  2.256988e-04  2.467021e-04  2.695772e-04
## pctWPubAsst            6.088175e-37  8.654274e-04  9.466341e-04  1.035181e-03
## pctWRetire            -1.382592e-37 -1.970227e-04 -2.155636e-04 -2.357871e-04
## medFamInc             -5.212376e-37 -7.346384e-04 -8.029026e-04 -8.772129e-04
## perCapInc             -4.335705e-37 -6.079549e-04 -6.641142e-04 -7.251823e-04
## whitePerCap           -2.636438e-37 -3.633597e-04 -3.962432e-04 -4.318697e-04
## blackPerCap           -3.776955e-37 -5.315993e-04 -5.809274e-04 -6.346050e-04
## indianPerCap          -1.297608e-37 -1.809827e-04 -1.975996e-04 -2.156481e-04
## AsianPerCap           -1.873830e-37 -2.600912e-04 -2.838313e-04 -3.095877e-04
## OtherPerCap           -1.555015e-37 -2.148030e-04 -2.342964e-04 -2.554236e-04
## HispPerCap            -3.143569e-37 -4.385148e-04 -4.787816e-04 -5.225138e-04
## NumUnderPov            8.232953e-37  1.176392e-03  1.287440e-03  1.408626e-03
## PctPopUnderPov         5.375555e-37  7.614053e-04  8.325766e-04  9.101143e-04
## PctLess9thGrade        4.534435e-37  6.404940e-04  7.001730e-04  7.651535e-04
## PctNotHSGrad           5.617285e-37  7.958449e-04  8.702608e-04  9.513351e-04
## PctBSorMore           -3.540036e-37 -4.988377e-04 -5.451977e-04 -5.956493e-04
## PctUnemployed          5.869563e-37  8.321810e-04  9.100605e-04  9.949162e-04
## PctEmploy             -4.484594e-37 -6.339463e-04 -6.930805e-04 -7.574710e-04
## PctEmplManu           -5.221756e-38 -7.760571e-05 -8.524785e-05 -9.364773e-05
## PctEmplProfServ       -9.587876e-38 -1.363901e-04 -1.492016e-04 -1.631716e-04
## PctOccupManu           3.496926e-37  4.916738e-04  5.372553e-04  5.868334e-04
## PctOccupMgmtProf      -4.283892e-37 -6.028657e-04 -6.588151e-04 -7.196796e-04
## MalePctDivorce         6.776730e-37  9.686741e-04  1.060169e-03  1.160022e-03
## MalePctNevMarr         4.085791e-37  5.814919e-04  6.361404e-04  6.957287e-04
## FemalePctDiv           7.470215e-37  1.067299e-03  1.168055e-03  1.278005e-03
## TotalPctDiv            7.085240e-37  1.012464e-03  1.108066e-03  1.212392e-03
## PersPerFam             2.141132e-37  3.046091e-04  3.332271e-04  3.644286e-04
## PctFam2Par            -8.233935e-37 -1.175747e-03 -1.286680e-03 -1.407715e-03
## PctKids2Par           -8.421443e-37 -1.203170e-03 -1.316761e-03 -1.440709e-03
## PctYoungKids2Par      -7.165698e-37 -1.022532e-03 -1.118944e-03 -1.224118e-03
## PctTeen2Par           -8.130017e-37 -1.162170e-03 -1.271960e-03 -1.391771e-03
## PctWorkMomYoungKids   -3.145356e-38 -4.503986e-05 -4.929633e-05 -5.394367e-05
## PctWorkMom            -2.021903e-37 -2.888493e-04 -3.161124e-04 -3.458608e-04
## NumIlleg               1.020060e-36  1.461189e-03  1.599532e-03  1.750561e-03
## PctIlleg               7.552614e-37  1.082208e-03  1.184716e-03  1.296634e-03
## NumImmig               7.942007e-37  1.133180e-03  1.239979e-03  1.356476e-03
## PctImmigRecent         1.846260e-37  2.605066e-04  2.847515e-04  3.111392e-04
## PctImmigRec5           2.410562e-37  3.406943e-04  3.724638e-04  4.070522e-04
## PctImmigRec8           2.898080e-37  4.108815e-04  4.493339e-04  4.912248e-04
## PctImmigRec10          3.518986e-37  4.997445e-04  5.466037e-04  5.976701e-04
## PctRecentImmig         2.303250e-37  3.281145e-04  3.589803e-04  3.926389e-04
## PctRecImmig5           2.470006e-37  3.521309e-04  3.852854e-04  4.214445e-04
## PctRecImmig8           2.516992e-37  3.593073e-04  3.931890e-04  4.301507e-04
## PctRecImmig10          2.648438e-37  3.781797e-04  4.138538e-04  4.527726e-04
## PctSpeakEnglOnly      -2.504801e-37 -3.553820e-04 -3.886660e-04 -4.249293e-04
## PctNotSpeakEnglWell    3.213509e-37  4.554343e-04  4.980396e-04  5.444476e-04
## PctLargHouseFam        4.591175e-37  6.536774e-04  7.151538e-04  7.821830e-04
## PctLargHouseOccup      3.637942e-37  5.173898e-04  5.659894e-04  6.189661e-04
## PersPerOccupHous      -5.515811e-38 -7.816894e-05 -8.548157e-05 -9.344769e-05
## PersPerOwnOccHous     -1.854046e-37 -2.649034e-04 -2.899175e-04 -3.172121e-04
## PersPerRentOccHous     3.087352e-37  4.388333e-04  4.800291e-04  5.249305e-04
## PctPersOwnOccup       -6.274799e-37 -8.909012e-04 -9.744264e-04 -1.065443e-03
## PctPersDenseHous       5.076525e-37  7.222647e-04  7.901376e-04  8.641288e-04
## PctHousLess3BR         6.473066e-37  9.189735e-04  1.005122e-03  1.098995e-03
## MedNumBR              -3.295940e-37 -4.670888e-04 -5.107872e-04 -5.583852e-04
## HousVacant             6.590959e-37  9.452846e-04  1.034910e-03  1.132774e-03
## PctHousOccup          -3.869374e-37 -5.534189e-04 -6.057368e-04 -6.628345e-04
## PctHousOwnOcc         -5.980952e-37 -8.483939e-04 -9.278518e-04 -1.014417e-03
## PctVacantBoarded       5.217656e-37  7.467312e-04  8.173721e-04  8.944755e-04
## PctVacMore6Mos         2.650289e-38  3.546443e-05  3.856491e-05  4.189909e-05
## MedYrHousBuilt        -1.113531e-37 -1.572228e-04 -1.718694e-04 -1.878099e-04
## PctHousNoPhone         4.731472e-37  6.712874e-04  7.341793e-04  8.027026e-04
## PctWOFullPlumb         4.157637e-37  5.893702e-04  6.445304e-04  7.046172e-04
## OwnOccLowQuart        -2.207887e-37 -3.081783e-04 -3.365214e-04 -3.672963e-04
## OwnOccMedVal          -1.938511e-37 -2.696782e-04 -2.943845e-04 -3.211907e-04
## OwnOccHiQuart         -1.721850e-37 -2.385527e-04 -2.603020e-04 -2.838782e-04
## RentLowQ              -2.702377e-37 -3.787034e-04 -4.136927e-04 -4.517162e-04
## RentMedian            -2.704412e-37 -3.774597e-04 -4.121703e-04 -4.498573e-04
## RentHighQ             -2.201768e-37 -3.069559e-04 -3.351457e-04 -3.657452e-04
## MedRent               -2.645172e-37 -3.684966e-04 -4.023071e-04 -4.390013e-04
## MedRentPctHousInc      4.513019e-37  6.439372e-04  7.046449e-04  7.708634e-04
## MedOwnCostPctInc       8.023320e-38  1.196257e-04  1.314370e-04  1.444263e-04
## MedOwnCostPctIncNoMtg  6.569571e-38  9.341161e-05  1.021826e-04  1.117423e-04
## NumInShelters          8.618281e-37  1.234493e-03  1.351375e-03  1.478966e-03
## NumStreet              7.976102e-37  1.144582e-03  1.253170e-03  1.371752e-03
## PctForeignBorn         1.979262e-37  2.835744e-04  3.104242e-04  3.397331e-04
## PctBornSameState      -8.886896e-38 -1.307974e-04 -1.435431e-04 -1.575280e-04
## PctSameHouse85        -2.016454e-37 -2.841717e-04 -3.105888e-04 -3.393288e-04
## PctSameCity85          8.867718e-38  1.289116e-04  1.413183e-04  1.549027e-04
## PctSameState85        -2.310264e-38 -3.365665e-05 -3.689941e-05 -4.044995e-05
## LandArea               4.231217e-37  6.072336e-04  6.648465e-04  7.277619e-04
## PopDens                3.260679e-37  4.659629e-04  5.099619e-04  5.579677e-04
## PctUsePubTrans         1.580487e-37  2.296451e-04  2.517242e-04  2.758926e-04
##                                                                              
## (Intercept)            2.408608e-01  2.411248e-01  2.414112e-01  2.417218e-01
## (Intercept)            .             .             .             .           
## state                 -5.809497e-06 -6.366290e-06 -6.975449e-06 -7.641702e-06
## fold                  -5.026316e-06 -5.499845e-06 -6.016354e-06 -6.579440e-06
## population             1.277161e-03  1.396835e-03  1.527228e-03  1.669205e-03
## householdsize         -9.759961e-05 -1.071050e-04 -1.175339e-04 -1.289751e-04
## racepctblack           1.107236e-03  1.211823e-03  1.325952e-03  1.450430e-03
## racePctWhite          -1.242253e-03 -1.359055e-03 -1.486404e-03 -1.625171e-03
## racePctAsian           8.393138e-05  9.224127e-05  1.013807e-04  1.114325e-04
## racePctHisp            5.486080e-04  5.991996e-04  6.541615e-04  7.138138e-04
## agePct12t21            1.552188e-04  1.680184e-04  1.816192e-04  1.960173e-04
## agePct12t29            4.517798e-04  4.920537e-04  5.355265e-04  5.823748e-04
## agePct16t24            2.476580e-04  2.692266e-04  2.924024e-04  3.172503e-04
## agePct65up             1.645788e-04  1.799125e-04  1.966036e-04  2.147590e-04
## numbUrban              1.251180e-03  1.368642e-03  1.496671e-03  1.636127e-03
## pctUrban               8.780469e-05  9.669937e-05  1.065234e-04  1.173784e-04
## medIncome             -8.753072e-04 -9.553519e-04 -1.042180e-03 -1.136262e-03
## pctWWage              -7.284557e-04 -7.958129e-04 -8.690290e-04 -9.485432e-04
## pctWFarmSelf          -3.371917e-04 -3.694159e-04 -4.046520e-04 -4.431678e-04
## pctWInvInc            -1.414723e-03 -1.545864e-03 -1.688478e-03 -1.843437e-03
## pctWSocSec             2.944539e-04  3.214843e-04  3.508276e-04  3.826496e-04
## pctWPubAsst            1.131630e-03  1.236610e-03  1.350790e-03  1.474872e-03
## pctWRetire            -2.578268e-04 -2.818299e-04 -3.079527e-04 -3.363602e-04
## medFamInc             -9.579953e-04 -1.045735e-03 -1.140937e-03 -1.244127e-03
## perCapInc             -7.914889e-04 -8.634098e-04 -9.413333e-04 -1.025658e-03
## whitePerCap           -4.703871e-04 -5.119679e-04 -5.567822e-04 -6.049939e-04
## blackPerCap           -6.929404e-04 -7.562794e-04 -8.249820e-04 -8.994205e-04
## indianPerCap          -2.352204e-04 -2.564209e-04 -2.793561e-04 -3.041342e-04
## AsianPerCap           -3.374829e-04 -3.676553e-04 -4.002441e-04 -4.353876e-04
## OtherPerCap           -2.782774e-04 -3.029636e-04 -3.295869e-04 -3.582492e-04
## HispPerCap            -5.699369e-04 -6.213029e-04 -6.768687e-04 -7.368941e-04
## NumUnderPov            1.540775e-03  1.684794e-03  1.841645e-03  2.012353e-03
## PctPopUnderPov         9.945026e-04  1.086275e-03  1.185993e-03  1.294244e-03
## PctLess9thGrade        8.358293e-04  9.126342e-04  9.960227e-04  1.086468e-03
## PctNotHSGrad           1.039578e-03  1.135550e-03  1.239839e-03  1.353062e-03
## PctBSorMore           -6.504941e-04 -7.100615e-04 -7.746948e-04 -8.447506e-04
## PctUnemployed          1.087290e-03  1.187770e-03  1.296979e-03  1.415567e-03
## PctEmploy             -8.275196e-04 -9.036609e-04 -9.863507e-04 -1.076064e-03
## PctEmplManu           -1.028824e-04 -1.130360e-04 -1.242008e-04 -1.364787e-04
## PctEmplProfServ       -1.783911e-04 -1.949607e-04 -2.129866e-04 -2.325813e-04
## PctOccupManu           6.407005e-04  6.991723e-04  7.625766e-04  8.312520e-04
## PctOccupMgmtProf      -7.858235e-04 -8.576381e-04 -9.355306e-04 -1.019922e-03
## MalePctDivorce         1.268924e-03  1.387626e-03  1.516929e-03  1.657683e-03
## MalePctNevMarr         7.606498e-04  8.313325e-04  9.082309e-04  9.918236e-04
## FemalePctDiv           1.397903e-03  1.528576e-03  1.670898e-03  1.825801e-03
## TotalPctDiv            1.326163e-03  1.450163e-03  1.585226e-03  1.732236e-03
## PersPerFam             3.984205e-04  4.354269e-04  4.756851e-04  5.194453e-04
## PctFam2Par            -1.539690e-03 -1.683504e-03 -1.840120e-03 -2.010556e-03
## PctKids2Par           -1.575876e-03 -1.723189e-03 -1.883639e-03 -2.058275e-03
## PctYoungKids2Par      -1.338781e-03 -1.463712e-03 -1.599741e-03 -1.747747e-03
## PctTeen2Par           -1.522440e-03 -1.664872e-03 -1.820026e-03 -1.988925e-03
## PctWorkMomYoungKids   -5.901190e-05 -6.453559e-05 -7.055136e-05 -7.709791e-05
## PctWorkMom            -3.782991e-04 -4.136492e-04 -4.521472e-04 -4.940435e-04
## NumIlleg               1.915348e-03  2.095048e-03  2.290897e-03  2.504207e-03
## PctIlleg               1.418760e-03  1.551957e-03  1.697146e-03  1.855307e-03
## NumImmig               1.483465e-03  1.621802e-03  1.772395e-03  1.936206e-03
## PctImmigRecent         3.398310e-04  3.710003e-04  4.048284e-04  4.415032e-04
## PctImmigRec5           4.446755e-04  4.855653e-04  5.299641e-04  5.781246e-04
## PctImmigRec8           5.368248e-04  5.864237e-04  6.403270e-04  6.988544e-04
## PctImmigRec10          6.532797e-04  7.137925e-04  7.795883e-04  8.510661e-04
## PctRecentImmig         4.293147e-04  4.692509e-04  5.127048e-04  5.599477e-04
## PctRecImmig5           4.608521e-04  5.037715e-04  5.504816e-04  6.012771e-04
## PctRecImmig8           4.704452e-04  5.143452e-04  5.621402e-04  6.141366e-04
## PctRecImmig10          4.952038e-04  5.414355e-04  5.917735e-04  6.465418e-04
## PctSpeakEnglOnly      -4.644079e-04 -5.073541e-04 -5.540335e-04 -6.047239e-04
## PctNotSpeakEnglWell    5.949591e-04  6.498939e-04  7.095879e-04  7.743929e-04
## PctLargHouseFam        8.552199e-04  9.347500e-04  1.021287e-03  1.115375e-03
## PctLargHouseOccup      6.766766e-04  7.395005e-04  8.078393e-04  8.821161e-04
## PersPerOccupHous      -1.021186e-04 -1.115494e-04 -1.217980e-04 -1.329251e-04
## PersPerOwnOccHous     -3.469782e-04 -3.794209e-04 -4.147587e-04 -4.532236e-04
## PersPerRentOccHous     5.738387e-04  6.270740e-04  6.849752e-04  7.478990e-04
## PctPersOwnOccup       -1.164554e-03 -1.272401e-03 -1.389661e-03 -1.517045e-03
## PctPersDenseHous       9.447383e-04  1.032498e-03  1.127971e-03  1.231751e-03
## PctHousLess3BR         1.201214e-03  1.312440e-03  1.433369e-03  1.564735e-03
## MedNumBR              -6.101935e-04 -6.665406e-04 -7.277724e-04 -7.942509e-04
## HousVacant             1.239583e-03  1.356093e-03  1.483115e-03  1.621513e-03
## PctHousOccup          -7.251163e-04 -7.930148e-04 -8.669917e-04 -9.475383e-04
## PctHousOwnOcc         -1.108661e-03 -1.211188e-03 -1.322633e-03 -1.443664e-03
## PctVacantBoarded       9.785901e-04  1.070303e-03  1.170241e-03  1.279072e-03
## PctVacMore6Mos         4.547703e-05  4.930760e-05  5.339802e-05  5.775329e-05
## MedYrHousBuilt        -2.051449e-04 -2.239801e-04 -2.444257e-04 -2.665963e-04
## PctHousNoPhone         8.773115e-04  9.584865e-04  1.046735e-03  1.142591e-03
## PctWOFullPlumb         7.700253e-04  8.411716e-04  9.184951e-04  1.002456e-03
## OwnOccLowQuart        -4.006782e-04 -4.368478e-04 -4.759905e-04 -5.182946e-04
## OwnOccMedVal          -3.502443e-04 -3.816961e-04 -4.156993e-04 -4.524086e-04
## OwnOccHiQuart         -3.094050e-04 -3.370076e-04 -3.668122e-04 -3.989439e-04
## RentLowQ              -4.929996e-04 -5.377770e-04 -5.862904e-04 -6.387884e-04
## RentMedian            -4.907352e-04 -5.350244e-04 -5.829510e-04 -6.347445e-04
## RentHighQ             -3.989263e-04 -4.348655e-04 -4.737432e-04 -5.157419e-04
## MedRent               -4.787833e-04 -5.218623e-04 -5.684517e-04 -6.187669e-04
## MedRentPctHousInc      8.430518e-04  9.216993e-04  1.007326e-03  1.100482e-03
## MedOwnCostPctInc       1.587122e-04  1.744258e-04  1.917113e-04  2.107273e-04
## MedOwnCostPctIncNoMtg  1.221550e-04  1.334885e-04  1.458147e-04  1.592090e-04
## NumInShelters          1.618177e-03  1.769985e-03  1.935430e-03  2.115622e-03
## NumStreet              1.501186e-03  1.642396e-03  1.796367e-03  1.964153e-03
## PctForeignBorn         3.717099e-04  4.065776e-04  4.445744e-04  4.859530e-04
## PctBornSameState      -1.728715e-04 -1.897041e-04 -2.081685e-04 -2.284207e-04
## PctSameHouse85        -3.705701e-04 -4.044994e-04 -4.413114e-04 -4.812076e-04
## PctSameCity85          1.697735e-04  1.860489e-04  2.038570e-04  2.233372e-04
## PctSameState85        -4.433635e-05 -4.858904e-05 -5.324091e-05 -5.832741e-05
## LandArea               7.964360e-04  8.713579e-04  9.530510e-04  1.042074e-03
## PopDens                6.103165e-04  6.673668e-04  7.294998e-04  7.971200e-04
## PctUsePubTrans         3.023410e-04  3.312761e-04  3.629218e-04  3.975198e-04
##                                                                              
## (Intercept)            2.420581e-01  2.424218e-01  2.428146e-01  2.432383e-01
## (Intercept)            .             .             .             .           
## state                 -8.370163e-06 -9.166362e-06 -1.003626e-05 -1.098630e-05
## fold                  -7.192945e-06 -7.860966e-06 -8.587862e-06 -9.378253e-06
## population             1.823683e-03  1.991629e-03  2.174057e-03  2.372030e-03
## householdsize         -1.415260e-04 -1.552929e-04 -1.703920e-04 -1.869500e-04
## racepctblack           1.586119e-03  1.733941e-03  1.894876e-03  2.069965e-03
## racePctWhite          -1.776282e-03 -1.940719e-03 -2.119523e-03 -2.313789e-03
## racePctAsian           1.224876e-04  1.346453e-04  1.480141e-04  1.627124e-04
## racePctHisp            7.784891e-04  8.485304e-04  9.242885e-04  1.006119e-03
## agePct12t21            2.111944e-04  2.271143e-04  2.437185e-04  2.609222e-04
## agePct12t29            6.327684e-04  6.868665e-04  7.448119e-04  8.067255e-04
## agePct16t24            3.438254e-04  3.721698e-04  4.023087e-04  4.342454e-04
## agePct65up             2.344911e-04  2.559183e-04  2.791644e-04  3.043583e-04
## numbUrban              1.787926e-03  1.953033e-03  2.132470e-03  2.327304e-03
## pctUrban               1.293773e-04  1.426461e-04  1.573252e-04  1.735709e-04
## medIncome             -1.238083e-03 -1.348135e-03 -1.466914e-03 -1.594914e-03
## pctWWage              -1.034814e-03 -1.128317e-03 -1.229545e-03 -1.339000e-03
## pctWFarmSelf          -4.852528e-04 -5.312186e-04 -5.814002e-04 -6.361574e-04
## pctWInvInc            -2.011657e-03 -2.194092e-03 -2.391728e-03 -2.605585e-03
## pctWSocSec             4.171220e-04  4.544211e-04  4.947267e-04  5.382204e-04
## pctWPubAsst            1.609593e-03  1.755723e-03  1.914059e-03  2.085422e-03
## pctWRetire            -3.672261e-04 -4.007324e-04 -4.370686e-04 -4.764312e-04
## medFamInc             -1.355845e-03 -1.476643e-03 -1.607079e-03 -1.747711e-03
## perCapInc             -1.116787e-03 -1.215128e-03 -1.321082e-03 -1.435042e-03
## whitePerCap           -6.567569e-04 -7.122104e-04 -7.714723e-04 -8.346333e-04
## blackPerCap           -9.799776e-04 -1.067043e-03 -1.161009e-03 -1.262268e-03
## indianPerCap          -3.308629e-04 -3.596486e-04 -3.905940e-04 -4.237961e-04
## AsianPerCap           -4.732208e-04 -5.138719e-04 -5.574595e-04 -6.040880e-04
## OtherPerCap           -3.890474e-04 -4.220704e-04 -4.573957e-04 -4.950858e-04
## HispPerCap            -8.016379e-04 -8.713543e-04 -9.462875e-04 -1.026666e-03
## NumUnderPov            2.197996e-03  2.399713e-03  2.618697e-03  2.856193e-03
## PctPopUnderPov         1.411639e-03  1.538813e-03  1.676417e-03  1.825113e-03
## PctLess9thGrade        1.184460e-03  1.290500e-03  1.405103e-03  1.528784e-03
## PctNotHSGrad           1.475863e-03  1.608907e-03  1.752879e-03  1.908481e-03
## PctBSorMore           -9.205959e-04 -1.002606e-03 -1.091161e-03 -1.186640e-03
## PctUnemployed          1.544215e-03  1.683627e-03  1.834530e-03  1.997668e-03
## PctEmploy             -1.173296e-03 -1.278553e-03 -1.392356e-03 -1.515232e-03
## PctEmplManu           -1.499820e-04 -1.648339e-04 -1.811703e-04 -1.991403e-04
## PctEmplProfServ       -2.538626e-04 -2.769540e-04 -3.019840e-04 -3.290853e-04
## PctOccupManu           9.055452e-04  9.858081e-04  1.072394e-03  1.165654e-03
## PctOccupMgmtProf      -1.111245e-03 -1.209940e-03 -1.316450e-03 -1.431216e-03
## MalePctDivorce         1.810790e-03  1.977202e-03  2.157918e-03  2.353986e-03
## MalePctNevMarr         1.082613e-03  1.181125e-03  1.287903e-03  1.403512e-03
## FemalePctDiv           1.994269e-03  2.177342e-03  2.376110e-03  2.591712e-03
## TotalPctDiv            1.892132e-03  2.065902e-03  2.254585e-03  2.459267e-03
## PersPerFam             5.669702e-04  6.185343e-04  6.744232e-04  7.349315e-04
## PctFam2Par            -2.195889e-03 -2.397256e-03 -2.615848e-03 -2.852909e-03
## PctKids2Par           -2.248211e-03 -2.454619e-03 -2.678732e-03 -2.921839e-03
## PctYoungKids2Par      -1.908658e-03 -2.083452e-03 -2.273154e-03 -2.478833e-03
## PctTeen2Par           -2.172651e-03 -2.372349e-03 -2.589218e-03 -2.824519e-03
## PctWorkMomYoungKids   -8.421585e-05 -9.194757e-05 -1.003370e-04 -1.094294e-04
## PctWorkMom            -5.396027e-04 -5.891033e-04 -6.428371e-04 -7.011085e-04
## NumIlleg               2.736375e-03  2.988877e-03  3.263270e-03  3.561187e-03
## PctIlleg               2.027485e-03  2.214788e-03  2.418385e-03  2.639508e-03
## NumImmig               2.114245e-03  2.307572e-03  2.517293e-03  2.744553e-03
## PctImmigRecent         4.812182e-04  5.241713e-04  5.705627e-04  6.205924e-04
## PctImmigRec5           6.303078e-04  6.867821e-04  7.478203e-04  8.136974e-04
## PctImmigRec8           7.623390e-04  8.311259e-04  9.055697e-04  9.860319e-04
## PctImmigRec10          9.286435e-04  1.012754e-03  1.103847e-03  1.202382e-03
## PctRecentImmig         6.112642e-04  6.669516e-04  7.273179e-04  7.926806e-04
## PctRecImmig5           6.564676e-04  7.163768e-04  7.813414e-04  8.517092e-04
## PctRecImmig8           6.706569e-04  7.320396e-04  7.986380e-04  8.708181e-04
## PctRecImmig10          7.060819e-04  7.707524e-04  8.409275e-04  9.169960e-04
## PctSpeakEnglOnly      -6.597153e-04 -7.193075e-04 -7.838089e-04 -8.535336e-04
## PctNotSpeakEnglWell    8.446746e-04  9.208110e-04  1.003190e-03  1.092206e-03
## PctLargHouseFam        1.217585e-03  1.328513e-03  1.448784e-03  1.579040e-03
## PctLargHouseOccup      9.627752e-04  1.050280e-03  1.145113e-03  1.247771e-03
## PersPerOccupHous      -1.449938e-04 -1.580697e-04 -1.722200e-04 -1.875132e-04
## PersPerOwnOccHous     -4.950618e-04 -5.405326e-04 -5.899093e-04 -6.434777e-04
## PersPerRentOccHous     8.162196e-04  8.903279e-04  9.706291e-04  1.057542e-03
## PctPersOwnOccup       -1.655296e-03 -1.805189e-03 -1.967524e-03 -2.143122e-03
## PctPersDenseHous       1.344461e-03  1.466752e-03  1.599303e-03  1.742811e-03
## PctHousLess3BR         1.707302e-03  1.861867e-03  2.029253e-03  2.210304e-03
## MedNumBR              -8.663531e-04 -9.444695e-04 -1.029001e-03 -1.120357e-03
## HousVacant             1.772205e-03  1.936167e-03  2.114428e-03  2.308074e-03
## PctHousOccup          -1.035176e-03 -1.130455e-03 -1.233956e-03 -1.346290e-03
## PctHousOwnOcc         -1.574978e-03 -1.717299e-03 -1.871373e-03 -2.037962e-03
## PctVacantBoarded       1.397503e-03  1.526286e-03  1.666209e-03  1.818103e-03
## PctVacMore6Mos         6.237562e-05  6.726363e-05  7.241162e-05  7.780856e-05
## MedYrHousBuilt        -2.906102e-04 -3.165883e-04 -3.446531e-04 -3.749275e-04
## PctHousNoPhone         1.246610e-03  1.359375e-03  1.481484e-03  1.613555e-03
## PctWOFullPlumb         1.093536e-03  1.192231e-03  1.299055e-03  1.414532e-03
## OwnOccLowQuart        -5.639495e-04 -6.131432e-04 -6.660591e-04 -7.228727e-04
## OwnOccMedVal          -4.919774e-04 -5.345555e-04 -5.802865e-04 -6.293039e-04
## OwnOccHiQuart         -4.335249e-04 -4.706718e-04 -5.104927e-04 -5.530835e-04
## RentLowQ              -6.955236e-04 -7.567506e-04 -8.227224e-04 -8.936870e-04
## RentMedian            -6.906351e-04 -7.508512e-04 -8.156145e-04 -8.851365e-04
## RentHighQ             -5.610442e-04 -6.098298e-04 -6.622727e-04 -7.185368e-04
## MedRent               -6.730224e-04 -7.314284e-04 -7.941869e-04 -8.614869e-04
## MedRentPctHousInc      1.201750e-03  1.311739e-03  1.431089e-03  1.560466e-03
## MedOwnCostPctInc       2.316484e-04  2.546664e-04  2.799918e-04  3.078553e-04
## MedOwnCostPctIncNoMtg  1.737504e-04  1.895211e-04  2.066060e-04  2.250922e-04
## NumInShelters          2.311736e-03  2.525019e-03  2.756782e-03  3.008400e-03
## NumStreet              2.146874e-03  2.345719e-03  2.561948e-03  2.796886e-03
## PctForeignBorn         5.309814e-04  5.799423e-04  6.331325e-04  6.908621e-04
## PctBornSameState      -2.506305e-04 -2.749831e-04 -3.016801e-04 -3.309402e-04
## PctSameHouse85        -5.243953e-04 -5.710855e-04 -6.214911e-04 -6.758241e-04
## PctSameCity85          2.446404e-04  2.679300e-04  2.933830e-04  3.211904e-04
## PctSameState85        -6.388665e-05 -6.995950e-05 -7.658962e-05 -8.382349e-05
## LandArea               1.139021e-03  1.244522e-03  1.359246e-03  1.483893e-03
## PopDens                8.706546e-04  9.505529e-04  1.037286e-03  1.131343e-03
## PctUsePubTrans         4.353312e-04  4.766367e-04  5.217384e-04  5.709598e-04
##                                                                              
## (Intercept)            2.436945e-01  2.441850e-01  2.447113e-01  2.452750e-01
## (Intercept)            .             .             .             .           
## state                 -1.202337e-05 -1.315491e-05 -1.438886e-05 -1.573371e-05
## fold                  -1.023703e-05 -1.116936e-05 -1.218066e-05 -1.327663e-05
## population             2.586656e-03  2.819076e-03  3.070466e-03  3.342023e-03
## householdsize         -2.051049e-04 -2.250070e-04 -2.468196e-04 -2.707196e-04
## racepctblack           2.260312e-03  2.467078e-03  2.691490e-03  2.934828e-03
## racePctWhite          -2.524669e-03 -2.753368e-03 -3.001141e-03 -3.269285e-03
## racePctAsian           1.788685e-04  1.966214e-04  2.161212e-04  2.375285e-04
## racePctHisp            1.094379e-03  1.189420e-03  1.291586e-03  1.401202e-03
## agePct12t21            2.786086e-04  2.966228e-04  3.147654e-04  3.327850e-04
## agePct12t29            8.726983e-04  9.427835e-04  1.016986e-03  1.095254e-03
## agePct16t24            4.679561e-04  5.033835e-04  5.404299e-04  5.789491e-04
## agePct65up             3.316334e-04  3.611270e-04  3.929796e-04  4.273334e-04
## numbUrban              2.538654e-03  2.767680e-03  3.015578e-03  3.283576e-03
## pctUrban               1.915572e-04  2.114777e-04  2.335476e-04  2.580054e-04
## medIncome             -1.732618e-03 -1.880492e-03 -2.038971e-03 -2.208449e-03
## pctWWage              -1.457193e-03 -1.584640e-03 -1.721852e-03 -1.869330e-03
## pctWFarmSelf          -6.958750e-04 -7.609645e-04 -8.318638e-04 -9.090381e-04
## pctWInvInc            -2.836703e-03 -3.086135e-03 -3.354941e-03 -3.644170e-03
## pctWSocSec             5.850836e-04  6.354949e-04  6.896277e-04  7.476466e-04
## pctWPubAsst            2.270655e-03  2.470611e-03  2.686146e-03  2.918115e-03
## pctWRetire            -5.190224e-04 -5.650488e-04 -6.147198e-04 -6.682452e-04
## medFamInc             -1.899091e-03 -2.061752e-03 -2.236201e-03 -2.422907e-03
## perCapInc             -1.557382e-03 -1.688449e-03 -1.828551e-03 -1.977947e-03
## whitePerCap           -9.017482e-04 -9.728273e-04 -1.047826e-03 -1.126633e-03
## blackPerCap           -1.371204e-03 -1.488186e-03 -1.613564e-03 -1.747656e-03
## indianPerCap          -4.593433e-04 -4.973124e-04 -5.377650e-04 -5.807438e-04
## AsianPerCap           -6.538426e-04 -7.067839e-04 -7.629412e-04 -8.223050e-04
## OtherPerCap           -5.351832e-04 -5.777050e-04 -6.226376e-04 -6.699289e-04
## HispPerCap            -1.112695e-03 -1.204550e-03 -1.302364e-03 -1.406221e-03
## NumUnderPov            3.113491e-03  3.391920e-03  3.692843e-03  4.017640e-03
## PctPopUnderPov         1.985574e-03  2.158468e-03  2.344457e-03  2.544181e-03
## PctLess9thGrade        1.662058e-03  1.805431e-03  1.959391e-03  2.124399e-03
## PctNotHSGrad           2.076419e-03  2.257404e-03  2.452134e-03  2.661293e-03
## PctBSorMore           -1.289421e-03 -1.399869e-03 -1.518331e-03 -1.645130e-03
## PctUnemployed          2.173792e-03  2.363661e-03  2.568022e-03  2.787609e-03
## PctEmploy             -1.647709e-03 -1.790312e-03 -1.943549e-03 -2.107910e-03
## PctEmplManu           -2.189077e-04 -2.406523e-04 -2.645711e-04 -2.908794e-04
## PctEmplProfServ       -3.583946e-04 -3.900512e-04 -4.241960e-04 -4.609703e-04
## PctOccupManu           1.265927e-03  1.373542e-03  1.488801e-03  1.611975e-03
## PctOccupMgmtProf      -1.554670e-03 -1.687229e-03 -1.829282e-03 -1.981183e-03
## MalePctDivorce         2.566496e-03  2.796579e-03  3.045400e-03  3.314152e-03
## MalePctNevMarr         1.528529e-03  1.663539e-03  1.809134e-03  1.965900e-03
## FemalePctDiv           2.825333e-03  3.078200e-03  3.351572e-03  3.646735e-03
## TotalPctDiv            2.681080e-03  2.921195e-03  3.180815e-03  3.461173e-03
## PersPerFam             8.003619e-04  8.710222e-04  9.472232e-04  1.029275e-03
## PctFam2Par            -3.109734e-03 -3.387658e-03 -3.688055e-03 -4.012324e-03
## PctKids2Par           -3.185282e-03 -3.470451e-03 -3.778776e-03 -4.111719e-03
## PctYoungKids2Par      -2.701599e-03 -2.942596e-03 -3.202997e-03 -3.483997e-03
## PctTeen2Par           -3.079563e-03 -3.355709e-03 -3.654361e-03 -3.976954e-03
## PctWorkMomYoungKids   -1.192706e-04 -1.299068e-04 -1.413835e-04 -1.537450e-04
## PctWorkMom            -7.642329e-04 -8.325355e-04 -9.063486e-04 -9.860095e-04
## NumIlleg               3.884336e-03  4.234493e-03  4.613494e-03  5.023223e-03
## PctIlleg               2.879449e-03  3.139557e-03  3.421234e-03  3.725931e-03
## NumImmig               2.990532e-03  3.256431e-03  3.543467e-03  3.852857e-03
## PctImmigRecent         6.744575e-04  7.323488e-04  7.944466e-04  8.609161e-04
## PctImmigRec5           8.846867e-04  9.610560e-04  1.043062e-03  1.130946e-03
## PctImmigRec8           1.072878e-03  1.166471e-03  1.267170e-03  1.375323e-03
## PctImmigRec10          1.308828e-03  1.423655e-03  1.547333e-03  1.680322e-03
## PctRecentImmig         8.633642e-04  9.396970e-04  1.022008e-03  1.110620e-03
## PctRecImmig5           9.278368e-04  1.010087e-03  1.098823e-03  1.194408e-03
## PctRecImmig8           9.489573e-04  1.033441e-03  1.124660e-03  1.223004e-03
## PctRecImmig10          9.993591e-04  1.088427e-03  1.184617e-03  1.288346e-03
## PctSpeakEnglOnly      -9.287983e-04 -1.009919e-03 -1.097204e-03 -1.190954e-03
## PctNotSpeakEnglWell    1.188255e-03  1.291732e-03  1.403021e-03  1.522493e-03
## PctLargHouseFam        1.719945e-03  1.872176e-03  2.036417e-03  2.213354e-03
## PctLargHouseOccup      1.358764e-03  1.478608e-03  1.607827e-03  1.746938e-03
## PersPerOccupHous      -2.040185e-04 -2.218047e-04 -2.409396e-04 -2.614885e-04
## PersPerOwnOccHous     -7.015359e-04 -7.643930e-04 -8.323680e-04 -9.057875e-04
## PersPerRentOccHous     1.151494e-03  1.252922e-03  1.362263e-03  1.479954e-03
## PctPersOwnOccup       -2.332819e-03 -2.537460e-03 -2.757886e-03 -2.994926e-03
## PctPersDenseHous       1.897994e-03  2.065582e-03  2.246310e-03  2.440911e-03
## PctHousLess3BR         2.405878e-03  2.616843e-03  2.844059e-03  3.088374e-03
## MedNumBR              -1.218949e-03 -1.325189e-03 -1.439483e-03 -1.562221e-03
## HousVacant             2.518240e-03  2.746113e-03  2.992925e-03  3.259948e-03
## PctHousOccup          -1.468092e-03 -1.600025e-03 -1.742775e-03 -1.897047e-03
## PctHousOwnOcc         -2.217839e-03 -2.411783e-03 -2.620561e-03 -2.844926e-03
## PctVacantBoarded       1.982837e-03  2.161315e-03  2.354472e-03  2.563273e-03
## PctVacMore6Mos         8.343711e-05  8.927247e-05  9.528115e-05  1.014197e-04
## MedYrHousBuilt        -4.075328e-04 -4.425864e-04 -4.802001e-04 -5.204762e-04
## PctHousNoPhone         1.756215e-03  1.910101e-03  2.075847e-03  2.254081e-03
## PctWOFullPlumb         1.539195e-03  1.673575e-03  1.818200e-03  1.973584e-03
## OwnOccLowQuart        -7.837473e-04 -8.488290e-04 -9.182416e-04 -9.920805e-04
## OwnOccMedVal          -6.817269e-04 -7.376564e-04 -7.971689e-04 -8.603111e-04
## OwnOccHiQuart         -5.985238e-04 -6.468724e-04 -6.981620e-04 -7.523933e-04
## RentLowQ              -9.698824e-04 -1.051532e-03 -1.138838e-03 -1.231975e-03
## RentMedian            -9.596127e-04 -1.039216e-03 -1.124092e-03 -1.214345e-03
## RentHighQ             -7.787719e-04 -8.431081e-04 -9.116500e-04 -9.844704e-04
## MedRent               -9.334982e-04 -1.010366e-03 -1.092201e-03 -1.179075e-03
## MedRentPctHousInc      1.700557e-03  1.852071e-03  2.015729e-03  2.192263e-03
## MedOwnCostPctInc       3.385094e-04  3.722299e-04  4.093174e-04  4.500985e-04
## MedOwnCostPctIncNoMtg  2.450682e-04  2.666230e-04  2.898448e-04  3.148199e-04
## NumInShelters          3.281311e-03  3.577007e-03  3.897030e-03  4.242959e-03
## NumStreet              3.051924e-03  3.328516e-03  3.628173e-03  3.952457e-03
## PctForeignBorn         7.534536e-04  8.212396e-04  8.945608e-04  9.737631e-04
## PctBornSameState      -3.630007e-04 -3.981183e-04 -4.365703e-04 -4.786547e-04
## PctSameHouse85        -7.342925e-04 -7.970961e-04 -8.644224e-04 -9.364413e-04
## PctSameCity85          3.515587e-04  3.847104e-04  4.208851e-04  4.603404e-04
## PctSameState85        -9.171035e-05 -1.003021e-04 -1.096531e-04 -1.198198e-04
## LandArea               1.619201e-03  1.765939e-03  1.924905e-03  2.096924e-03
## PopDens                1.233233e-03  1.343477e-03  1.462609e-03  1.591167e-03
## PctUsePubTrans         6.246469e-04  6.831683e-04  7.469158e-04  8.163037e-04
##                                                                              
## (Intercept)            2.458796e-01  2.465226e-01  2.472068e-01  2.479331e-01
## (Intercept)            .             .             .             .           
## state                 -1.720086e-05 -1.879597e-05 -2.053106e-05 -2.241701e-05
## fold                  -1.447101e-05 -1.575664e-05 -1.714615e-05 -1.864640e-05
## population             3.636433e-03  3.952367e-03  4.292185e-03  4.657071e-03
## householdsize         -2.967233e-04 -3.253337e-04 -3.566352e-04 -3.908608e-04
## racepctblack           3.199640e-03  3.485242e-03  3.794035e-03  4.127525e-03
## racePctWhite          -3.560567e-03 -3.873900e-03 -4.211811e-03 -4.575730e-03
## racePctAsian           2.607053e-04  2.863698e-04  3.144658e-04  3.451877e-04
## racePctHisp            1.519581e-03  1.645254e-03  1.779250e-03  1.921755e-03
## agePct12t21            3.514859e-04  3.685679e-04  3.844598e-04  3.986366e-04
## agePct12t29            1.178850e-03  1.265176e-03  1.355025e-03  1.448001e-03
## agePct16t24            6.196422e-04  6.606793e-04  7.024327e-04  7.444822e-04
## agePct65up             4.648286e-04  5.047537e-04  5.476428e-04  5.936405e-04
## numbUrban              3.573882e-03  3.886097e-03  4.222236e-03  4.583545e-03
## pctUrban               2.848506e-04  3.148329e-04  3.480644e-04  3.848963e-04
## medIncome             -2.391278e-03 -2.584258e-03 -2.789173e-03 -3.006135e-03
## pctWWage              -2.028900e-03 -2.198705e-03 -2.380230e-03 -2.573868e-03
## pctWFarmSelf          -9.930816e-04 -1.084339e-03 -1.183434e-03 -1.290940e-03
## pctWInvInc            -3.957017e-03 -4.290728e-03 -4.647958e-03 -5.029598e-03
## pctWSocSec             8.103480e-04  8.767545e-04  9.474945e-04  1.022667e-03
## pctWPubAsst            3.168867e-03  3.436586e-03  3.723239e-03  4.029545e-03
## pctWRetire            -7.260578e-04 -7.879734e-04 -8.543664e-04 -9.254248e-04
## medFamInc             -2.623850e-03 -2.836661e-03 -3.062848e-03 -3.302589e-03
## perCapInc             -2.138181e-03 -2.307029e-03 -2.485588e-03 -2.673787e-03
## whitePerCap           -1.210087e-03 -1.296116e-03 -1.385152e-03 -1.476697e-03
## blackPerCap           -1.891637e-03 -2.044171e-03 -2.206141e-03 -2.377642e-03
## indianPerCap          -6.266338e-04 -6.747883e-04 -7.254584e-04 -7.785677e-04
## AsianPerCap           -8.853780e-04 -9.510757e-04 -1.019668e-03 -1.090907e-03
## OtherPerCap           -7.199875e-04 -7.717810e-04 -8.255006e-04 -8.808657e-04
## HispPerCap            -1.516934e-03 -1.633066e-03 -1.755106e-03 -1.882812e-03
## NumUnderPov            4.368697e-03  4.745663e-03  5.150687e-03  5.585086e-03
## PctPopUnderPov         2.759112e-03  2.988312e-03  3.232975e-03  3.493541e-03
## PctLess9thGrade        2.301659e-03  2.490174e-03  2.690874e-03  2.903990e-03
## PctNotHSGrad           2.886302e-03  3.126422e-03  3.382819e-03  3.655972e-03
## PctBSorMore           -1.781015e-03 -1.925418e-03 -2.078899e-03 -2.241582e-03
## PctUnemployed          3.023750e-03  3.276001e-03  3.545468e-03  3.832674e-03
## PctEmploy             -2.284217e-03 -2.472227e-03 -2.672595e-03 -2.885602e-03
## PctEmplManu           -3.197854e-04 -3.515941e-04 -3.865619e-04 -4.249913e-04
## PctEmplProfServ       -5.006680e-04 -5.431594e-04 -5.886990e-04 -6.374133e-04
## PctOccupManu           1.743618e-03  1.883333e-03  2.031498e-03  2.188151e-03
## PctOccupMgmtProf      -2.143554e-03 -2.316076e-03 -2.499181e-03 -2.692947e-03
## MalePctDivorce         3.604566e-03  3.916950e-03  4.252943e-03  4.613753e-03
## MalePctNevMarr         2.134862e-03  2.315793e-03  2.509579e-03  2.716704e-03
## FemalePctDiv           3.965496e-03  4.308269e-03  4.676759e-03  5.072241e-03
## TotalPctDiv            3.763870e-03  4.089532e-03  4.439695e-03  4.815590e-03
## PersPerFam             1.117657e-03  1.212364e-03  1.313818e-03  1.422287e-03
## PctFam2Par            -4.362228e-03 -4.738565e-03 -5.143029e-03 -5.577000e-03
## PctKids2Par           -4.471019e-03 -4.857706e-03 -5.273480e-03 -5.719808e-03
## PctYoungKids2Par      -3.786927e-03 -4.112752e-03 -4.462775e-03 -4.838155e-03
## PctTeen2Par           -4.325074e-03 -4.699955e-03 -5.103177e-03 -5.536194e-03
## PctWorkMomYoungKids   -1.671645e-04 -1.814555e-04 -1.967536e-04 -2.130864e-04
## PctWorkMom            -1.072002e-03 -1.164411e-03 -1.263681e-03 -1.370135e-03
## NumIlleg               5.465859e-03  5.942887e-03  6.456464e-03  7.008517e-03
## PctIlleg               4.055228e-03  4.410478e-03  4.793306e-03  5.205266e-03
## NumImmig               4.186231e-03  4.543999e-03  4.927611e-03  5.338087e-03
## PctImmigRecent         9.320664e-04  1.007726e-03  1.088111e-03  1.173266e-03
## PctImmigRec5           1.225087e-03  1.325387e-03  1.432124e-03  1.545397e-03
## PctImmigRec8           1.491404e-03  1.615453e-03  1.747855e-03  1.888829e-03
## PctImmigRec10          1.823196e-03  1.976145e-03  2.139657e-03  2.314072e-03
## PctRecentImmig         1.206060e-03  1.308260e-03  1.417664e-03  1.534515e-03
## PctRecImmig5           1.297372e-03  1.407750e-03  1.525999e-03  1.652409e-03
## PctRecImmig8           1.329004e-03  1.442787e-03  1.564824e-03  1.695450e-03
## PctRecImmig10          1.400144e-03  1.520217e-03  1.649045e-03  1.786991e-03
## PctSpeakEnglOnly      -1.291527e-03 -1.399036e-03 -1.513766e-03 -1.635894e-03
## PctNotSpeakEnglWell    1.650536e-03  1.787380e-03  1.933327e-03  2.088589e-03
## PctLargHouseFam        2.403660e-03  2.607999e-03  2.827005e-03  3.061275e-03
## PctLargHouseOccup      1.896426e-03  2.056820e-03  2.228572e-03  2.412111e-03
## PersPerOccupHous      -2.835241e-04 -3.070780e-04 -3.322090e-04 -3.589545e-04
## PersPerOwnOccHous     -9.849714e-04 -1.070270e-03 -1.162012e-03 -1.260528e-03
## PersPerRentOccHous     1.606398e-03  1.742046e-03  1.887276e-03  2.042450e-03
## PctPersOwnOccup       -3.249337e-03 -3.521937e-03 -3.813401e-03 -4.124347e-03
## PctPersDenseHous       2.650069e-03  2.874536e-03  3.114951e-03  3.371924e-03
## PctHousLess3BR         3.350543e-03  3.631431e-03  3.931706e-03  4.251990e-03
## MedNumBR              -1.693735e-03 -1.834424e-03 -1.984559e-03 -2.144385e-03
## HousVacant             3.548442e-03  3.859796e-03  4.195322e-03  4.556355e-03
## PctHousOccup          -2.063511e-03 -2.242979e-03 -2.436152e-03 -2.643765e-03
## PctHousOwnOcc         -3.085499e-03 -3.343107e-03 -3.618288e-03 -3.911562e-03
## PctVacantBoarded       2.788634e-03  3.031667e-03  3.293333e-03  3.574637e-03
## PctVacMore6Mos         1.075945e-04  1.137981e-04  1.199207e-04  1.258627e-04
## MedYrHousBuilt        -5.634821e-04 -6.093274e-04 -6.580479e-04 -7.096695e-04
## PctHousNoPhone         2.445303e-03  2.650275e-03  2.869462e-03  3.103354e-03
## PctWOFullPlumb         2.140139e-03  2.318455e-03  2.508887e-03  2.711792e-03
## OwnOccLowQuart        -1.070290e-03 -1.153072e-03 -1.240309e-03 -1.331903e-03
## OwnOccMedVal          -9.269847e-04 -9.973318e-04 -1.071187e-03 -1.148405e-03
## OwnOccHiQuart         -8.094279e-04 -8.693462e-04 -9.319371e-04 -9.970016e-04
## RentLowQ              -1.330969e-03 -1.436100e-03 -1.547326e-03 -1.664628e-03
## RentMedian            -1.309927e-03 -1.411026e-03 -1.517497e-03 -1.629202e-03
## RentHighQ             -1.061520e-03 -1.142919e-03 -1.228535e-03 -1.318230e-03
## MedRent               -1.270925e-03 -1.367845e-03 -1.469660e-03 -1.576172e-03
## MedRentPctHousInc      2.382353e-03  2.586808e-03  2.806297e-03  3.041500e-03
## MedOwnCostPctInc       4.949468e-04  5.442112e-04  5.983173e-04  6.577042e-04
## MedOwnCostPctIncNoMtg  3.416233e-04  3.703431e-04  4.010419e-04  4.337763e-04
## NumInShelters          4.616334e-03  5.018890e-03  5.452193e-03  5.917838e-03
## NumStreet              4.302915e-03  4.681275e-03  5.089148e-03  5.528192e-03
## PctForeignBorn         1.059186e-03  1.151186e-03  1.250093e-03  1.356229e-03
## PctBornSameState      -5.247047e-04 -5.750405e-04 -6.300360e-04 -6.900768e-04
## PctSameHouse85        -1.013268e-03 -1.095067e-03 -1.181895e-03 -1.273780e-03
## PctSameCity85          5.033465e-04  5.502105e-04  6.012451e-04  6.567892e-04
## PctSameState85        -1.308678e-04 -1.428445e-04 -1.558152e-04 -1.698393e-04
## LandArea               2.282813e-03  2.483476e-03  2.699761e-03  2.932531e-03
## PopDens                1.729666e-03  1.878676e-03  2.038697e-03  2.210219e-03
## PctUsePubTrans         8.917765e-04  9.737789e-04  1.062794e-03  1.159315e-03
##                                                                              
## (Intercept)            2.487018e-01  2.495131e-01  2.503669e-01  2.512626e-01
## (Intercept)            .             .             .             .           
## state                 -2.446522e-05 -2.668772e-05 -2.909708e-05 -3.170640e-05
## fold                  -2.026451e-05 -2.200787e-05 -2.388417e-05 -2.590137e-05
## population             5.048160e-03  5.466511e-03  5.913091e-03  6.388740e-03
## householdsize         -4.282577e-04 -4.690864e-04 -5.136195e-04 -5.621391e-04
## racepctblack           4.487262e-03  4.874834e-03  5.291855e-03  5.739963e-03
## racePctWhite          -4.967097e-03 -5.387343e-03 -5.837883e-03 -6.320101e-03
## racePctAsian           3.787347e-04  4.153078e-04  4.551061e-04  4.983222e-04
## racePctHisp            2.072896e-03  2.232722e-03  2.401194e-03  2.578173e-03
## agePct12t21            4.104817e-04  4.192781e-04  4.242010e-04  4.243112e-04
## agePct12t29            1.543576e-03  1.641082e-03  1.739691e-03  1.838400e-03
## agePct16t24            7.863114e-04  8.272966e-04  8.666962e-04  9.036405e-04
## agePct65up             6.428870e-04  6.955158e-04  7.516516e-04  8.114079e-04
## numbUrban              4.971234e-03  5.386454e-03  5.830276e-03  6.303662e-03
## pctUrban               4.257136e-04  4.709367e-04  5.210236e-04  5.764703e-04
## medIncome             -3.235141e-03 -3.476051e-03 -3.728572e-03 -3.992239e-03
## pctWWage              -2.779955e-03 -2.998759e-03 -3.230464e-03 -3.475161e-03
## pctWFarmSelf          -1.407450e-03 -1.533577e-03 -1.669954e-03 -1.817220e-03
## pctWInvInc            -5.436443e-03 -5.869176e-03 -6.328345e-03 -6.814339e-03
## pctWSocSec             1.102344e-03  1.186561e-03  1.275317e-03  1.368566e-03
## pctWPubAsst            4.356150e-03  4.703605e-03  5.072348e-03  5.462681e-03
## pctWRetire            -1.001322e-03 -1.082213e-03 -1.168232e-03 -1.259483e-03
## medFamInc             -3.555940e-03 -3.822819e-03 -4.102983e-03 -4.396012e-03
## perCapInc             -2.871425e-03 -3.078156e-03 -3.293469e-03 -3.516676e-03
## whitePerCap           -1.570111e-03 -1.664606e-03 -1.759226e-03 -1.852831e-03
## blackPerCap           -2.558685e-03 -2.749176e-03 -2.948910e-03 -3.157551e-03
## indianPerCap          -8.339965e-04 -8.915769e-04 -9.510881e-04 -1.012252e-03
## AsianPerCap           -1.164460e-03 -1.239898e-03 -1.316680e-03 -1.394151e-03
## OtherPerCap           -9.375141e-04 -9.949921e-04 -1.052744e-03 -1.110103e-03
## HispPerCap            -2.015822e-03 -2.153636e-03 -2.295604e-03 -2.440905e-03
## NumUnderPov            6.050108e-03  6.546904e-03  7.076500e-03  7.639765e-03
## PctPopUnderPov         3.770355e-03  4.063651e-03  4.373535e-03  4.699963e-03
## PctLess9thGrade        3.129650e-03  3.367872e-03  3.618538e-03  3.881379e-03
## PctNotHSGrad           3.946269e-03  4.253985e-03  4.579264e-03  4.922100e-03
## PctBSorMore           -2.413511e-03 -2.594635e-03 -2.784801e-03 -2.983737e-03
## PctUnemployed          4.138047e-03  4.461901e-03  4.804408e-03  5.165585e-03
## PctEmploy             -3.111446e-03 -3.350219e-03 -3.601895e-03 -3.866312e-03
## PctEmplManu           -4.672088e-04 -5.135666e-04 -5.644425e-04 -6.202402e-04
## PctEmplProfServ       -6.894195e-04 -7.448227e-04 -8.037139e-04 -8.661667e-04
## PctOccupManu           2.353240e-03  2.526610e-03  2.707986e-03  2.896966e-03
## PctOccupMgmtProf      -2.897349e-03 -3.112240e-03 -3.337328e-03 -3.572171e-03
## MalePctDivorce         5.000558e-03  5.414491e-03  5.856625e-03  6.327948e-03
## MalePctNevMarr         2.937601e-03  3.172631e-03  3.422069e-03  3.686093e-03
## FemalePctDiv           5.495946e-03  5.949045e-03  6.432624e-03  6.947669e-03
## TotalPctDiv            5.218410e-03  5.649294e-03  6.109309e-03  6.599428e-03
## PersPerFam             1.538014e-03  1.661209e-03  1.792051e-03  1.930674e-03
## PctFam2Par            -6.041815e-03 -6.538742e-03 -7.068962e-03 -7.633547e-03
## PctKids2Par           -6.198117e-03 -6.709775e-03 -7.256071e-03 -7.838194e-03
## PctYoungKids2Par      -5.240011e-03 -5.669397e-03 -6.127289e-03 -6.614564e-03
## PctTeen2Par           -6.000422e-03 -6.497226e-03 -7.027897e-03 -7.593632e-03
## PctWorkMomYoungKids   -2.304722e-04 -2.489181e-04 -2.684166e-04 -2.889432e-04
## PctWorkMom            -1.484074e-03 -1.605781e-03 -1.735505e-03 -1.873462e-03
## NumIlleg               7.600931e-03  8.235516e-03  8.913980e-03  9.637893e-03
## PctIlleg               5.647911e-03  6.122778e-03  6.631373e-03  7.175153e-03
## NumImmig               5.776334e-03  6.243108e-03  6.738979e-03  7.264289e-03
## PctImmigRecent         1.263185e-03  1.357804e-03  1.456992e-03  1.560543e-03
## PctImmigRec5           1.665249e-03  1.791650e-03  1.924491e-03  2.063569e-03
## PctImmigRec8           2.038539e-03  2.197078e-03  2.364460e-03  2.540603e-03
## PctImmigRec10          2.499667e-03  2.696652e-03  2.905147e-03  3.125177e-03
## PctRecentImmig         1.659020e-03  1.791330e-03  1.931534e-03  2.079648e-03
## PctRecImmig5           1.787230e-03  1.930662e-03  2.082840e-03  2.243830e-03
## PctRecImmig8           1.834962e-03  1.983614e-03  2.141603e-03  2.309056e-03
## PctRecImmig10          1.934385e-03  2.091512e-03  2.258600e-03  2.435808e-03
## PctSpeakEnglOnly      -1.765540e-03 -1.902761e-03 -2.047531e-03 -2.199734e-03
## PctNotSpeakEnglWell    2.253303e-03  2.427524e-03  2.611206e-03  2.804187e-03
## PctLargHouseFam        3.311351e-03  3.577712e-03  3.860752e-03  4.160774e-03
## PctLargHouseOccup      2.607820e-03  2.816024e-03  3.036981e-03  3.270863e-03
## PersPerOccupHous      -3.873390e-04 -4.173709e-04 -4.490393e-04 -4.823102e-04
## PersPerOwnOccHous     -1.366141e-03 -1.479162e-03 -1.599885e-03 -1.728581e-03
## PersPerRentOccHous     2.207896e-03  2.383891e-03  2.570661e-03  2.768364e-03
## PctPersOwnOccup       -4.455298e-03 -4.806653e-03 -5.178666e-03 -5.571425e-03
## PctPersDenseHous       3.646002e-03  3.937650e-03  4.247234e-03  4.575005e-03
## PctHousLess3BR         4.592801e-03  4.954526e-03  5.337398e-03  5.741471e-03
## MedNumBR              -2.314083e-03 -2.493754e-03 -2.683410e-03 -2.882952e-03
## HousVacant             4.944215e-03  5.360189e-03  5.805514e-03  6.281354e-03
## PctHousOccup          -2.866546e-03 -3.105206e-03 -3.360434e-03 -3.632887e-03
## PctHousOwnOcc         -4.223344e-03 -4.553923e-03 -4.903434e-03 -5.271839e-03
## PctVacantBoarded       3.876577e-03  4.200128e-03  4.546230e-03  4.915780e-03
## PctVacMore6Mos         1.315074e-04  1.367197e-04  1.413453e-04  1.452101e-04
## MedYrHousBuilt        -7.641863e-04 -8.215549e-04 -8.816878e-04 -9.444464e-04
## PctHousNoPhone         3.352380e-03  3.616890e-03  3.897141e-03  4.193285e-03
## PctWOFullPlumb         2.927452e-03  3.156068e-03  3.397736e-03  3.652437e-03
## OwnOccLowQuart        -1.427693e-03 -1.527440e-03 -1.630823e-03 -1.737435e-03
## OwnOccMedVal          -1.228771e-03 -1.312000e-03 -1.397722e-03 -1.485484e-03
## OwnOccHiQuart         -1.064273e-03 -1.133409e-03 -1.203987e-03 -1.275497e-03
## RentLowQ              -1.787910e-03 -1.917001e-03 -2.051639e-03 -2.191463e-03
## RentMedian            -1.745913e-03 -1.867303e-03 -1.992941e-03 -2.122272e-03
## RentHighQ             -1.411791e-03 -1.508922e-03 -1.609236e-03 -1.712244e-03
## MedRent               -1.687088e-03 -1.802007e-03 -1.920412e-03 -2.041655e-03
## MedRentPctHousInc      3.293055e-03  3.561548e-03  3.847495e-03  4.151331e-03
## MedOwnCostPctInc       7.228388e-04  7.942141e-04  8.723465e-04  9.577721e-04
## MedOwnCostPctIncNoMtg  4.685888e-04  5.055033e-04  5.445220e-04  5.856200e-04
## NumInShelters          6.417373e-03  6.952275e-03  7.523922e-03  8.133562e-03
## NumStreet              6.000048e-03  6.506325e-03  7.048575e-03  7.628269e-03
## PctForeignBorn         1.469893e-03  1.591356e-03  1.720846e-03  1.858547e-03
## PctBornSameState      -7.555692e-04 -8.269383e-04 -9.046257e-04 -9.890863e-04
## PctSameHouse85        -1.370697e-03 -1.472557e-03 -1.579197e-03 -1.690368e-03
## PctSameCity85          7.172040e-04  7.828742e-04  8.542078e-04  9.316370e-04
## PctSameState85        -1.849747e-04 -2.012759e-04 -2.187929e-04 -2.375689e-04
## LandArea               3.182639e-03  3.450911e-03  3.738139e-03  4.045058e-03
## PopDens                2.393699e-03  2.589546e-03  2.798108e-03  3.019658e-03
## PctUsePubTrans         1.263850e-03  1.376915e-03  1.499032e-03  1.630718e-03
##                                                                              
## (Intercept)            2.521990e-01  2.531746e-01  2.541875e-01  2.552350e-01
## (Intercept)            .             .             .             .           
## state                 -3.452930e-05 -3.757982e-05 -4.087244e-05 -4.442194e-05
## fold                  -2.806779e-05 -3.039210e-05 -3.288339e-05 -3.555127e-05
## population             6.894142e-03  7.429799e-03  7.995990e-03  8.592743e-03
## householdsize         -6.149338e-04 -6.722942e-04 -7.345075e-04 -8.018500e-04
## racepctblack           6.220811e-03  6.736057e-03  7.287356e-03  7.876353e-03
## racePctWhite          -6.835334e-03 -7.384856e-03 -7.969869e-03 -8.591483e-03
## racePctAsian           5.451361e-04  5.957086e-04  6.501731e-04  7.086272e-04
## racePctHisp            2.763400e-03  2.956493e-03  3.156925e-03  3.364020e-03
## agePct12t21            4.185511e-04  4.057415e-04  3.845826e-04  3.536575e-04
## agePct12t29            1.936016e-03  2.031143e-03  2.122172e-03  2.207275e-03
## agePct16t24            9.371227e-04  9.659919e-04  9.889478e-04  1.004539e-03
## agePct65up             8.748842e-04  9.421636e-04  1.013309e-03  1.088360e-03
## numbUrban              6.807439e-03  7.342267e-03  7.908606e-03  8.506683e-03
## pctUrban               6.378120e-04  7.056232e-04  7.805167e-04  8.631431e-04
## medIncome             -4.266395e-03 -4.550180e-03 -4.842512e-03 -5.142080e-03
## pctWWage              -3.732836e-03 -4.003351e-03 -4.286441e-03 -4.581699e-03
## pctWFarmSelf          -1.976026e-03 -2.147019e-03 -2.330839e-03 -2.528106e-03
## pctWInvInc            -7.327366e-03 -7.867434e-03 -8.434332e-03 -9.027615e-03
## pctWSocSec             1.466215e-03  1.568118e-03  1.674073e-03  1.783817e-03
## pctWPubAsst            5.874757e-03  6.308555e-03  6.763863e-03  7.240263e-03
## pctWRetire            -1.356044e-03 -1.457956e-03 -1.565227e-03 -1.677825e-03
## medFamInc             -4.701286e-03 -5.017974e-03 -5.345013e-03 -5.681106e-03
## perCapInc             -3.746888e-03 -3.983007e-03 -4.223717e-03 -4.467472e-03
## whitePerCap           -1.944090e-03 -2.031470e-03 -2.113227e-03 -2.187406e-03
## blackPerCap           -3.374626e-03 -3.599515e-03 -3.831440e-03 -4.069469e-03
## indianPerCap          -1.074729e-03 -1.138118e-03 -1.201954e-03 -1.265709e-03
## AsianPerCap           -1.471523e-03 -1.547875e-03 -1.622136e-03 -1.693091e-03
## OtherPerCap           -1.166282e-03 -1.220365e-03 -1.271299e-03 -1.317896e-03
## HispPerCap            -2.588539e-03 -2.737307e-03 -2.885809e-03 -3.032430e-03
## NumUnderPov            8.237378e-03  8.869792e-03  9.537200e-03  1.023950e-02
## PctPopUnderPov         5.042724e-03  5.401422e-03  5.775461e-03  6.164034e-03
## PctLess9thGrade        4.155960e-03  4.441659e-03  4.737658e-03  5.042926e-03
## PctNotHSGrad           5.282320e-03  5.659564e-03  6.053273e-03  6.462674e-03
## PctBSorMore           -3.191039e-03 -3.406166e-03 -3.628429e-03 -3.856985e-03
## PctUnemployed          5.545267e-03  5.943090e-03  6.358471e-03  6.790594e-03
## PctEmploy             -4.143159e-03 -4.431958e-03 -4.732055e-03 -5.042606e-03
## PctEmplManu           -6.813890e-04 -7.483432e-04 -8.215803e-04 -9.015999e-04
## PctEmplProfServ       -9.322353e-04 -1.001952e-03 -1.075326e-03 -1.152339e-03
## PctOccupManu           3.093001e-03  3.295392e-03  3.503278e-03  3.715631e-03
## PctOccupMgmtProf      -3.816151e-03 -4.068469e-03 -4.328132e-03 -4.593946e-03
## MalePctDivorce         6.829351e-03  7.361607e-03  7.925351e-03  8.521065e-03
## MalePctNevMarr         3.964760e-03  4.257999e-03  4.565590e-03  4.887151e-03
## FemalePctDiv           7.495036e-03  8.075433e-03  8.689397e-03  9.337269e-03
## TotalPctDiv            7.120512e-03  7.673287e-03  8.258326e-03  8.876026e-03
## PersPerFam             2.077164e-03  2.231557e-03  2.393832e-03  2.563911e-03
## PctFam2Par            -8.233440e-03 -8.869428e-03 -9.542126e-03 -1.025195e-02
## PctKids2Par           -8.457209e-03 -9.114040e-03 -9.809448e-03 -1.054401e-02
## PctYoungKids2Par      -7.131981e-03 -7.680164e-03 -8.259586e-03 -8.870551e-03
## PctTeen2Par           -8.195510e-03 -8.834476e-03 -9.511312e-03 -1.022663e-02
## PctWorkMomYoungKids   -3.104523e-04 -3.328742e-04 -3.561108e-04 -3.800321e-04
## PctWorkMom            -2.019823e-03 -2.174710e-03 -2.338187e-03 -2.510255e-03
## NumIlleg               1.040865e-02  1.122744e-02  1.209519e-02  1.301254e-02
## PctIlleg               7.755510e-03  8.373759e-03  9.031117e-03  9.728693e-03
## NumImmig               7.819112e-03  8.403202e-03  9.015953e-03  9.656349e-03
## PctImmigRecent         1.668165e-03  1.779471e-03  1.893975e-03  2.011084e-03
## PctImmigRec5           2.208577e-03  2.359095e-03  2.514575e-03  2.674336e-03
## PctImmigRec8           2.725321e-03  2.918306e-03  3.119122e-03  3.327192e-03
## PctImmigRec10          3.356649e-03  3.599344e-03  3.852905e-03  4.116821e-03
## PctRecentImmig         2.235598e-03  2.399209e-03  2.570193e-03  2.748134e-03
## PctRecImmig5           2.413606e-03  2.592042e-03  2.778901e-03  2.973814e-03
## PctRecImmig8           2.486021e-03  2.672449e-03  2.868185e-03  3.072953e-03
## PctRecImmig10          2.623214e-03  2.820801e-03  3.028445e-03  3.245898e-03
## PctSpeakEnglOnly      -2.359147e-03 -2.525429e-03 -2.698105e-03 -2.876558e-03
## PctNotSpeakEnglWell    3.006172e-03  3.216721e-03  3.435230e-03  3.660918e-03
## PctLargHouseFam        4.477966e-03  4.812396e-03  5.163990e-03  5.532528e-03
## PctLargHouseOccup      3.517752e-03  3.777623e-03  4.050340e-03  4.335644e-03
## PersPerOccupHous      -5.171215e-04 -5.533787e-04 -5.909492e-04 -6.296564e-04
## PersPerOwnOccHous     -1.865496e-03 -2.010838e-03 -2.164781e-03 -2.327449e-03
## PersPerRentOccHous     2.977089e-03  3.196843e-03  3.427545e-03  3.669027e-03
## PctPersOwnOccup       -5.984825e-03 -6.418545e-03 -6.872028e-03 -7.344465e-03
## PctPersDenseHous       4.921081e-03  5.285428e-03  5.667850e-03  6.067970e-03
## PctHousLess3BR         6.166592e-03  6.612380e-03  7.078197e-03  7.563131e-03
## MedNumBR              -3.092165e-03 -3.310697e-03 -3.538046e-03 -3.773553e-03
## HousVacant             6.788777e-03  7.328736e-03  7.902037e-03  8.509324e-03
## PctHousOccup          -3.923185e-03 -4.231897e-03 -4.559542e-03 -4.906575e-03
## PctHousOwnOcc         -5.658900e-03 -6.064155e-03 -6.486898e-03 -6.926160e-03
## PctVacantBoarded       5.309618e-03  5.728512e-03  6.173150e-03  6.644130e-03
## PctVacMore6Mos         1.481199e-04  1.498609e-04  1.502008e-04  1.488895e-04
## MedYrHousBuilt        -1.009634e-03 -1.076988e-03 -1.146175e-03 -1.216778e-03
## PctHousNoPhone         4.505356e-03  4.833255e-03  5.176745e-03  5.535438e-03
## PctWOFullPlumb         3.920018e-03  4.200182e-03  4.492469e-03  4.796253e-03
## OwnOccLowQuart        -1.846773e-03 -1.958239e-03 -2.071142e-03 -2.184696e-03
## OwnOccMedVal          -1.574741e-03 -1.664854e-03 -1.755092e-03 -1.844637e-03
## OwnOccHiQuart         -1.347336e-03 -1.418811e-03 -1.489135e-03 -1.557434e-03
## RentLowQ              -2.336014e-03 -2.484721e-03 -2.636908e-03 -2.791794e-03
## RentMedian            -2.254620e-03 -2.389178e-03 -2.525004e-03 -2.661027e-03
## RentHighQ             -1.817355e-03 -1.923863e-03 -2.030955e-03 -2.137703e-03
## MedRent               -2.164954e-03 -2.289380e-03 -2.413859e-03 -2.537170e-03
## MedRentPctHousInc      4.473394e-03  4.813907e-03  5.172967e-03  5.550532e-03
## MedOwnCostPctInc       1.051041e-03  1.152713e-03  1.263343e-03  1.383481e-03
## MedOwnCostPctIncNoMtg  6.287401e-04  6.737875e-04  7.206228e-04  7.690558e-04
## NumInShelters          8.782278e-03  9.470956e-03  1.020025e-02  1.097052e-02
## NumStreet              8.246772e-03  8.905315e-03  9.604966e-03  1.034660e-02
## PctForeignBorn         2.004580e-03  2.158996e-03  2.321763e-03  2.492754e-03
## PctBornSameState      -1.080784e-03 -1.180188e-03 -1.287762e-03 -1.403966e-03
## PctSameHouse85        -1.805733e-03 -1.924849e-03 -2.047169e-03 -2.172031e-03
## PctSameCity85          1.015618e-03  1.106631e-03  1.205180e-03  1.311791e-03
## PctSameState85        -2.576382e-04 -2.790238e-04 -3.017349e-04 -3.257640e-04
## LandArea               4.372337e-03  4.720556e-03  5.090188e-03  5.481579e-03
## PopDens                3.254379e-03  3.502350e-03  3.763526e-03  4.037724e-03
## PctUsePubTrans         1.772481e-03  1.924809e-03  2.088158e-03  2.262942e-03
##                                                                              
## (Intercept)            2.563142e-01  2.574217e-01  2.585537e-01  2.597061e-01
## (Intercept)            .             .             .             .           
## state                 -4.824339e-05 -5.235203e-05 -5.676320e-05 -6.149221e-05
## fold                  -3.840593e-05 -4.145829e-05 -4.472013e-05 -4.820430e-05
## population             9.219795e-03  9.876561e-03  1.056210e-02  1.127507e-02
## householdsize         -8.745783e-04 -9.529183e-04 -1.037053e-03 -1.127107e-03
## racepctblack           8.504676e-03  9.173925e-03  9.885667e-03  1.064143e-02
## racePctWhite          -9.250707e-03 -9.948437e-03 -1.068544e-02 -1.146237e-02
## racePctAsian           7.711225e-04  8.376540e-04  9.081493e-04  9.824575e-04
## racePctHisp            3.576942e-03  3.794686e-03  4.016082e-03  4.239788e-03
## agePct12t21            3.114406e-04  2.563096e-04  1.865630e-04  1.004426e-04
## agePct12t29            2.284400e-03  2.351275e-03  2.405420e-03  2.444152e-03
## agePct16t24            1.011162e-03  1.007073e-03  9.903912e-04  9.591180e-04
## agePct65up             1.167331e-03  1.250204e-03  1.336925e-03  1.427403e-03
## numbUrban              9.136459e-03  9.797593e-03  1.048941e-02  1.121085e-02
## pctUrban               9.541879e-04  1.054369e-03  1.164433e-03  1.285150e-03
## medIncome             -5.447337e-03 -5.756500e-03 -6.067549e-03 -6.378238e-03
## pctWWage              -4.888567e-03 -5.206334e-03 -5.534127e-03 -5.870915e-03
## pctWFarmSelf          -2.739413e-03 -2.965312e-03 -3.206297e-03 -3.462797e-03
## pctWInvInc            -9.646593e-03 -1.029032e-02 -1.095762e-02 -1.164703e-02
## pctWSocSec             1.897029e-03  2.013321e-03  2.132246e-03  2.253292e-03
## pctWPubAsst            7.737118e-03  8.253562e-03  8.788492e-03  9.340569e-03
## pctWRetire            -1.795678e-03 -1.918681e-03 -2.046690e-03 -2.179535e-03
## medFamInc             -6.024708e-03 -6.374031e-03 -6.727045e-03 -7.081492e-03
## perCapInc             -4.712499e-03 -4.956801e-03 -5.198168e-03 -5.434195e-03
## whitePerCap           -2.251846e-03 -2.304184e-03 -2.341877e-03 -2.362220e-03
## blackPerCap           -4.312507e-03 -4.559308e-03 -4.808484e-03 -5.058519e-03
## indianPerCap          -1.328797e-03 -1.390580e-03 -1.450376e-03 -1.507472e-03
## AsianPerCap           -1.759371e-03 -1.819460e-03 -1.871697e-03 -1.914286e-03
## OtherPerCap           -1.358822e-03 -1.392605e-03 -1.417636e-03 -1.432179e-03
## HispPerCap            -3.175343e-03 -3.312510e-03 -3.441689e-03 -3.560452e-03
## NumUnderPov            1.097625e-02  1.174667e-02  1.254954e-02  1.338326e-02
## PctPopUnderPov         6.566110e-03  6.980433e-03  7.405519e-03  7.839666e-03
## PctLess9thGrade        5.356216e-03  5.676061e-03  6.000771e-03  6.328445e-03
## PctNotHSGrad           6.886772e-03  7.324348e-03  7.773960e-03  8.233952e-03
## PctBSorMore           -4.090835e-03 -4.328828e-03 -4.569665e-03 -4.811912e-03
## PctUnemployed          7.238396e-03  7.700562e-03  8.175518e-03  8.661440e-03
## PctEmploy             -5.362577e-03 -5.690738e-03 -6.025664e-03 -6.365748e-03
## PctEmplManu           -9.889201e-04 -1.084075e-03 -1.187612e-03 -1.300083e-03
## PctEmplProfServ       -1.232948e-03 -1.317081e-03 -1.404639e-03 -1.495497e-03
## PctOccupManu           3.931255e-03  4.148791e-03  4.366721e-03  4.583389e-03
## PctOccupMgmtProf      -4.864513e-03 -5.138237e-03 -5.413332e-03 -5.687837e-03
## MalePctDivorce         9.149059e-03  9.809460e-03  1.050220e-02  1.122700e-02
## MalePctNevMarr         5.222131e-03  5.569796e-03  5.929230e-03  6.299330e-03
## FemalePctDiv           1.001918e-02  1.073502e-02  1.148445e-02  1.226686e-02
## TotalPctDiv            9.526594e-03  1.021003e-02  1.092611e-02  1.167439e-02
## PersPerFam             2.741660e-03  2.926886e-03  3.119349e-03  3.318764e-03
## PctFam2Par            -1.099913e-02 -1.178365e-02 -1.260527e-02 -1.346354e-02
## PctKids2Par           -1.131811e-02 -1.213194e-02 -1.298544e-02 -1.387839e-02
## PctYoungKids2Par      -9.513187e-03 -1.018743e-02 -1.089304e-02 -1.162956e-02
## PctTeen2Par           -1.098084e-02 -1.177415e-02 -1.260655e-02 -1.347781e-02
## PctWorkMomYoungKids   -4.044716e-04 -4.292230e-04 -4.540365e-04 -4.786159e-04
## PctWorkMom            -2.690849e-03 -2.879827e-03 -3.076979e-03 -3.282017e-03
## NumIlleg               1.397979e-02  1.499688e-02  1.606335e-02  1.717830e-02
## PctIlleg               1.046747e-02  1.124832e-02  1.207194e-02  1.293894e-02
## NumImmig               1.032292e-02  1.101368e-02  1.172612e-02  1.245714e-02
## PctImmigRecent         2.130090e-03  2.250174e-03  2.370404e-03  2.489737e-03
## PctImmigRec5           2.837559e-03  3.003279e-03  3.170386e-03  3.337630e-03
## PctImmigRec8           3.541791e-03  3.762041e-03  3.986908e-03  4.215206e-03
## PctImmigRec10          4.390422e-03  4.672871e-03  4.963167e-03  5.260139e-03
## PctRecentImmig         2.932477e-03  3.122521e-03  3.317407e-03  3.516115e-03
## PctRecImmig5           3.176279e-03  3.385640e-03  3.601088e-03  3.821648e-03
## PctRecImmig8           3.286346e-03  3.507812e-03  3.736652e-03  3.972007e-03
## PctRecImmig10          3.472783e-03  3.708575e-03  3.952603e-03  4.204034e-03
## PctSpeakEnglOnly      -3.060014e-03 -3.247537e-03 -3.438023e-03 -3.630195e-03
## PctNotSpeakEnglWell    3.892818e-03  4.129768e-03  4.370404e-03  4.613166e-03
## PctLargHouseFam        5.917634e-03  6.318768e-03  6.735233e-03  7.166169e-03
## PctLargHouseOccup      4.633149e-03  4.942341e-03  5.262580e-03  5.593103e-03
## PersPerOccupHous      -6.692731e-04 -7.095148e-04 -7.500320e-04 -7.904031e-04
## PersPerOwnOccHous     -2.498919e-03 -2.679207e-03 -2.868269e-03 -3.065993e-03
## PersPerRentOccHous     3.921028e-03  4.183193e-03  4.455085e-03  4.736185e-03
## PctPersOwnOccup       -7.834774e-03 -8.341598e-03 -8.863295e-03 -9.397945e-03
## PctPersDenseHous       6.485225e-03  6.918861e-03  7.367927e-03  7.831286e-03
## PctHousLess3BR         8.065981e-03  8.585239e-03  9.119091e-03  9.665414e-03
## MedNumBR              -4.016388e-03 -4.265547e-03 -4.519843e-03 -4.777913e-03
## HousVacant             9.151045e-03  9.827437e-03  1.053850e-02  1.128396e-02
## PctHousOccup          -5.273391e-03 -5.660313e-03 -6.067598e-03 -6.495436e-03
## PctHousOwnOcc         -7.380690e-03 -7.848952e-03 -8.329111e-03 -8.819044e-03
## PctVacantBoarded       7.141946e-03  7.666984e-03  8.219517e-03  8.799703e-03
## PctVacMore6Mos         1.456617e-04  1.402394e-04  1.323345e-04  1.216526e-04
## MedYrHousBuilt        -1.288295e-03 -1.360133e-03 -1.431596e-03 -1.501889e-03
## PctHousNoPhone         5.908798e-03  6.296134e-03  6.696612e-03  7.109257e-03
## PctWOFullPlumb         5.110725e-03  5.434893e-03  5.767580e-03  6.107427e-03
## OwnOccLowQuart        -2.298029e-03 -2.410197e-03 -2.520194e-03 -2.626977e-03
## OwnOccMedVal          -1.932584e-03 -2.017961e-03 -2.099735e-03 -2.176839e-03
## OwnOccHiQuart         -1.622750e-03 -1.684055e-03 -1.740264e-03 -1.790256e-03
## RentLowQ              -2.948494e-03 -3.106035e-03 -3.263371e-03 -3.419396e-03
## RentMedian            -2.796047e-03 -2.928749e-03 -3.057718e-03 -3.181455e-03
## RentHighQ             -2.243075e-03 -2.345939e-03 -2.445079e-03 -2.539209e-03
## MedRent               -2.657947e-03 -2.774690e-03 -2.885777e-03 -2.989484e-03
## MedRentPctHousInc      5.946407e-03  6.360237e-03  6.791501e-03  7.239512e-03
## MedOwnCostPctInc       1.513650e-03  1.654338e-03  1.805982e-03  1.968951e-03
## MedOwnCostPctIncNoMtg  8.188369e-04  8.696499e-04  9.211023e-04  9.727159e-04
## NumInShelters          1.178186e-02  1.263398e-02  1.352624e-02  1.445759e-02
## NumStreet              1.113089e-02  1.195824e-02  1.282881e-02  1.374246e-02
## PctForeignBorn         2.671737e-03  2.858363e-03  3.052157e-03  3.252514e-03
## PctBornSameState      -1.529236e-03 -1.663988e-03 -1.808597e-03 -1.963395e-03
## PctSameHouse85        -2.298655e-03 -2.426143e-03 -2.553484e-03 -2.679556e-03
## PctSameCity85          1.427009e-03  1.551400e-03  1.685542e-03  1.830026e-03
## PctSameState85        -3.510843e-04 -3.776470e-04 -4.053791e-04 -4.341812e-04
## LandArea               5.894931e-03  6.330274e-03  6.787453e-03  7.266103e-03
## PopDens                4.324607e-03  4.623675e-03  4.934242e-03  5.255438e-03
## PctUsePubTrans         2.449520e-03  2.648179e-03  2.859117e-03  3.082432e-03
##                                                                              
## (Intercept)            2.608745e-01  2.620619e-01  2.632518e-01  2.644454e-01
## (Intercept)            .             .             .             .           
## state                 -6.655428e-05 -7.195888e-05 -7.773107e-05 -8.388060e-05
## fold                  -5.192490e-05 -5.588670e-05 -6.013112e-05 -6.466699e-05
## population             1.201373e-02  1.277450e-02  1.355791e-02  1.435942e-02
## householdsize         -1.223131e-03 -1.327394e-03 -1.435775e-03 -1.549780e-03
## racepctblack           1.144269e-02  1.229003e-02  1.318709e-02  1.413417e-02
## racePctWhite          -1.227971e-02 -1.313733e-02 -1.403725e-02 -1.497878e-02
## racePctAsian           1.060338e-03  1.141685e-03  1.225333e-03  1.311042e-03
## racePctHisp            4.464299e-03  4.687311e-03  4.908774e-03  5.125998e-03
## agePct12t21           -3.838690e-06 -1.302403e-04 -2.760548e-04 -4.449385e-04
## agePct12t29            2.464620e-03  2.463154e-03  2.438691e-03  2.387147e-03
## agePct16t24            9.111589e-04  8.442488e-04  7.569434e-04  6.467077e-04
## agePct65up             1.521500e-03  1.618132e-03  1.719006e-03  1.822954e-03
## numbUrban              1.196049e-02  1.273786e-02  1.353882e-02  1.436144e-02
## pctUrban               1.417309e-03  1.562119e-03  1.719484e-03  1.890614e-03
## medIncome             -6.686115e-03 -6.988933e-03 -7.284670e-03 -7.570069e-03
## pctWWage              -6.215505e-03 -6.567475e-03 -6.924760e-03 -7.286013e-03
## pctWFarmSelf          -3.735151e-03 -4.023972e-03 -4.328812e-03 -4.649908e-03
## pctWInvInc            -1.235690e-02 -1.308771e-02 -1.383486e-02 -1.459728e-02
## pctWSocSec             2.375888e-03  2.500530e-03  2.625123e-03  2.749582e-03
## pctWPubAsst            9.908219e-03  1.049234e-02  1.108736e-02  1.169269e-02
## pctWRetire            -2.317020e-03 -2.459215e-03 -2.605546e-03 -2.755985e-03
## medFamInc             -7.434904e-03 -7.788458e-03 -8.133880e-03 -8.470707e-03
## perCapInc             -5.662308e-03 -5.883669e-03 -6.089724e-03 -6.280179e-03
## whitePerCap           -2.362377e-03 -2.342989e-03 -2.295590e-03 -2.219641e-03
## blackPerCap           -5.307794e-03 -5.558224e-03 -5.802410e-03 -6.041126e-03
## indianPerCap          -1.561139e-03 -1.612102e-03 -1.657378e-03 -1.697327e-03
## AsianPerCap           -1.945314e-03 -1.964878e-03 -1.967574e-03 -1.952734e-03
## OtherPerCap           -1.434380e-03 -1.424288e-03 -1.396714e-03 -1.350966e-03
## HispPerCap            -3.666208e-03 -3.759609e-03 -3.832432e-03 -3.884180e-03
## NumUnderPov            1.424576e-02  1.513828e-02  1.605195e-02  1.698596e-02
## PctPopUnderPov         8.280964e-03  8.731509e-03  9.182277e-03  9.633874e-03
## PctLess9thGrade        6.656986e-03  6.987643e-03  7.312314e-03  7.630963e-03
## PctNotHSGrad           8.702467e-03  9.181719e-03  9.662560e-03  1.014577e-02
## PctBSorMore           -5.054016e-03 -5.297584e-03 -5.535438e-03 -5.768215e-03
## PctUnemployed          9.156260e-03  9.661646e-03  1.016848e-02  1.067699e-02
## PctEmploy             -6.709215e-03 -7.057492e-03 -7.402755e-03 -7.745386e-03
## PctEmplManu           -1.422045e-03 -1.553852e-03 -1.696391e-03 -1.850043e-03
## PctEmplProfServ       -1.589503e-03 -1.686950e-03 -1.786929e-03 -1.889549e-03
## PctOccupManu           4.797013e-03  5.008586e-03  5.211212e-03  5.405077e-03
## PctOccupMgmtProf      -5.959643e-03 -6.229568e-03 -6.489963e-03 -6.740795e-03
## MalePctDivorce         1.198338e-02  1.277443e-02  1.359294e-02  1.444062e-02
## MalePctNevMarr         6.678813e-03  7.068191e-03  7.462667e-03  7.861925e-03
## FemalePctDiv           1.308138e-02  1.393064e-02  1.480690e-02  1.571131e-02
## TotalPctDiv            1.245418e-02  1.326786e-02  1.410863e-02  1.497759e-02
## PersPerFam             3.524816e-03  3.737259e-03  3.955593e-03  4.179555e-03
## PctFam2Par            -1.435776e-02 -1.529061e-02 -1.625475e-02 -1.725163e-02
## PctKids2Par           -1.481031e-02 -1.578352e-02 -1.679192e-02 -1.783680e-02
## PctYoungKids2Par      -1.239639e-02 -1.319490e-02 -1.402013e-02 -1.487273e-02
## PctTeen2Par           -1.438748e-02 -1.533677e-02 -1.632132e-02 -1.734155e-02
## PctWorkMomYoungKids   -5.026161e-04 -5.254179e-04 -5.470531e-04 -5.668261e-04
## PctWorkMom            -3.494581e-03 -3.714500e-03 -3.940875e-03 -4.173351e-03
## NumIlleg               1.834034e-02  1.954941e-02  2.079997e-02  2.209053e-02
## PctIlleg               1.384975e-02  1.480580e-02  1.580517e-02  1.684886e-02
## NumImmig               1.320306e-02  1.396016e-02  1.472250e-02  1.548492e-02
## PctImmigRecent         2.607029e-03  2.721576e-03  2.831168e-03  2.934830e-03
## PctImmigRec5           3.503627e-03  3.667438e-03  3.826452e-03  3.979400e-03
## PctImmigRec8           4.445601e-03  4.677183e-03  4.907369e-03  5.134896e-03
## PctImmigRec10          5.562462e-03  5.869234e-03  6.177804e-03  6.486916e-03
## PctRecentImmig         3.717460e-03  3.920144e-03  4.122692e-03  4.323466e-03
## PctRecImmig5           4.046184e-03  4.273586e-03  4.502164e-03  4.730437e-03
## PctRecImmig8           4.212860e-03  4.458295e-03  4.706628e-03  4.956564e-03
## PctRecImmig10          4.461878e-03  4.725315e-03  4.992547e-03  5.262370e-03
## PctSpeakEnglOnly      -3.822610e-03 -4.014158e-03 -4.202219e-03 -4.385284e-03
## PctNotSpeakEnglWell    4.856296e-03  5.098379e-03  5.336314e-03  5.568276e-03
## PctLargHouseFam        7.610574e-03  8.067821e-03  8.535577e-03  9.012949e-03
## PctLargHouseOccup      5.933039e-03  6.281526e-03  6.637161e-03  6.998980e-03
## PersPerOccupHous      -8.301261e-04 -8.692632e-04 -9.059472e-04 -9.399036e-04
## PersPerOwnOccHous     -3.272194e-03 -3.487207e-03 -3.709526e-03 -3.939223e-03
## PersPerRentOccHous     5.025910e-03  5.323730e-03  5.628638e-03  5.940081e-03
## PctPersOwnOccup       -9.943361e-03 -1.049789e-02 -1.105716e-02 -1.161904e-02
## PctPersDenseHous       8.307624e-03  8.795496e-03  9.293010e-03  9.798544e-03
## PctHousLess3BR         1.022179e-02  1.078584e-02  1.135368e-02  1.192248e-02
## MedNumBR              -5.038219e-03 -5.299122e-03 -5.558473e-03 -5.814397e-03
## HousVacant             1.206331e-02  1.287598e-02  1.372023e-02  1.459490e-02
## PctHousOccup          -6.943953e-03 -7.413543e-03 -7.903458e-03 -8.413986e-03
## PctHousOwnOcc         -9.316344e-03 -9.818063e-03 -1.032140e-02 -1.082314e-02
## PctVacantBoarded       9.407581e-03  1.004319e-02  1.070590e-02  1.139558e-02
## PctVacMore6Mos         1.078966e-04  9.119296e-05  7.036860e-05  4.549604e-05
## MedYrHousBuilt        -1.570110e-03 -1.635065e-03 -1.695856e-03 -1.751151e-03
## PctHousNoPhone         7.532975e-03  7.966486e-03  8.408260e-03  8.857017e-03
## PctWOFullPlumb         6.452903e-03  6.801958e-03  7.153079e-03  7.504120e-03
## OwnOccLowQuart        -2.729487e-03 -2.826799e-03 -2.917322e-03 -3.000336e-03
## OwnOccMedVal          -2.248189e-03 -2.312514e-03 -2.368773e-03 -2.415964e-03
## OwnOccHiQuart         -1.832894e-03 -1.866604e-03 -1.890742e-03 -1.904055e-03
## RentLowQ              -3.572976e-03 -3.722250e-03 -3.866983e-03 -4.005695e-03
## RentMedian            -3.298408e-03 -3.406037e-03 -3.504055e-03 -3.590318e-03
## RentHighQ             -2.626994e-03 -2.706175e-03 -2.776676e-03 -2.836578e-03
## MedRent               -3.084006e-03 -3.166389e-03 -3.236370e-03 -3.291351e-03
## MedRentPctHousInc      7.703416e-03  8.181592e-03  8.673727e-03  9.178124e-03
## MedOwnCostPctInc       2.143527e-03  2.330239e-03  2.528605e-03  2.738783e-03
## MedOwnCostPctIncNoMtg  1.023917e-03  1.073846e-03  1.121976e-03  1.167255e-03
## NumInShelters          1.542653e-02  1.643046e-02  1.746797e-02  1.853575e-02
## NumStreet              1.469876e-02  1.569636e-02  1.673509e-02  1.781321e-02
## PctForeignBorn         3.458689e-03  3.669775e-03  3.884776e-03  4.102531e-03
## PctBornSameState      -2.128656e-03 -2.304864e-03 -2.491747e-03 -2.689542e-03
## PctSameHouse85        -2.803135e-03 -2.922570e-03 -3.036986e-03 -3.144725e-03
## PctSameCity85          1.985447e-03  2.152179e-03  2.331143e-03  2.522753e-03
## PctSameState85        -4.639265e-04 -4.946536e-04 -5.258927e-04 -5.575696e-04
## LandArea               7.765632e-03  8.284924e-03  8.823294e-03  9.379189e-03
## PopDens                5.586194e-03  5.924898e-03  6.270598e-03  6.621399e-03
## PctUsePubTrans         3.318097e-03  3.566079e-03  3.825879e-03  4.097096e-03
##                                                                              
## (Intercept)            2.656385e-01  2.668276e-01  2.680098e-01  2.691825e-01
## (Intercept)            .             .             .             .           
## state                 -9.042150e-05 -9.736727e-05 -1.047308e-04 -1.125244e-04
## fold                  -6.951724e-05 -7.470781e-05 -8.026800e-05 -8.623082e-05
## population             1.517555e-02  1.600238e-02  1.683557e-02  1.767031e-02
## householdsize         -1.668978e-03 -1.792758e-03 -1.920310e-03 -2.050605e-03
## racepctblack           1.513266e-02  1.618389e-02  1.728912e-02  1.844955e-02
## racePctWhite          -1.596205e-02 -1.698709e-02 -1.805382e-02 -1.916209e-02
## racePctAsian           1.398077e-03  1.485573e-03  1.572542e-03  1.657875e-03
## racePctHisp            5.337087e-03  5.540096e-03  5.733064e-03  5.914036e-03
## agePct12t21           -6.383028e-04 -8.573585e-04 -1.103073e-03 -1.376129e-03
## agePct12t29            2.305490e-03  2.190749e-03  2.040070e-03  1.850760e-03
## agePct16t24            5.114886e-04  3.493405e-04  1.584699e-04 -6.271842e-05
## agePct65up             1.929686e-03  2.038860e-03  2.150081e-03  2.262897e-03
## numbUrban              1.520263e-02  1.605879e-02  1.692593e-02  1.779959e-02
## pctUrban               2.076274e-03  2.277205e-03  2.494125e-03  2.727720e-03
## medIncome             -7.842369e-03 -8.098801e-03 -8.336611e-03 -8.553095e-03
## pctWWage              -7.649673e-03 -8.014100e-03 -8.377581e-03 -8.738344e-03
## pctWFarmSelf          -4.987101e-03 -5.340054e-03 -5.708242e-03 -6.090929e-03
## pctWInvInc            -1.537289e-02 -1.615958e-02 -1.695518e-02 -1.775753e-02
## pctWSocSec             2.873229e-03  2.995359e-03  3.115249e-03  3.232154e-03
## pctWPubAsst            1.230615e-02  1.292542e-02  1.354807e-02  1.417155e-02
## pctWRetire            -2.910388e-03 -3.068658e-03 -3.230762e-03 -3.396754e-03
## medFamInc             -8.796134e-03 -9.107332e-03 -9.401489e-03 -9.675840e-03
## perCapInc             -6.452312e-03 -6.603446e-03 -6.730997e-03 -6.832511e-03
## whitePerCap           -2.112301e-03 -1.970832e-03 -1.792641e-03 -1.575334e-03
## blackPerCap           -6.272737e-03 -6.495675e-03 -6.708481e-03 -6.909836e-03
## indianPerCap          -1.731398e-03 -1.759138e-03 -1.780214e-03 -1.794447e-03
## AsianPerCap           -1.918239e-03 -1.861979e-03 -1.781882e-03 -1.675949e-03
## OtherPerCap           -1.284954e-03 -1.196578e-03 -1.083749e-03 -9.444197e-04
## HispPerCap            -3.911996e-03 -3.913042e-03 -3.884552e-03 -3.823872e-03
## NumUnderPov            1.793647e-02  1.889922e-02  1.986949e-02  2.084214e-02
## PctPopUnderPov         1.008385e-02  1.052966e-02  1.096872e-02  1.139841e-02
## PctLess9thGrade        7.940992e-03  8.239741e-03  8.524519e-03  8.792646e-03
## PctNotHSGrad           1.062898e-02  1.110978e-02  1.158570e-02  1.205430e-02
## PctBSorMore           -5.994127e-03 -6.211380e-03 -6.418212e-03 -6.612921e-03
## PctUnemployed          1.118436e-02  1.168761e-02  1.218366e-02  1.266934e-02
## PctEmploy             -8.083125e-03 -8.413651e-03 -8.734624e-03 -9.043713e-03
## PctEmplManu           -2.015323e-03 -2.192721e-03 -2.382701e-03 -2.585690e-03
## PctEmplProfServ       -1.994599e-03 -2.101857e-03 -2.211094e-03 -2.322077e-03
## PctOccupManu           5.588159e-03  5.758453e-03  5.914004e-03  6.052957e-03
## PctOccupMgmtProf      -6.979637e-03 -7.204076e-03 -7.411766e-03 -7.600479e-03
## MalePctDivorce         1.531616e-02  1.621806e-02  1.714463e-02  1.809398e-02
## MalePctNevMarr         8.264147e-03  8.667441e-03  9.069873e-03  9.469508e-03
## FemalePctDiv           1.664192e-02  1.759655e-02  1.857271e-02  1.956769e-02
## TotalPctDiv            1.587310e-02  1.679327e-02  1.773600e-02  1.869895e-02
## PersPerFam             4.408850e-03  4.643231e-03  4.882525e-03  5.126648e-03
## PctFam2Par            -1.827971e-02 -1.933732e-02 -2.042261e-02 -2.153365e-02
## PctKids2Par           -1.891694e-02 -2.003097e-02 -2.117741e-02 -2.235467e-02
## PctYoungKids2Par      -1.575141e-02 -1.665473e-02 -1.758118e-02 -1.852919e-02
## PctTeen2Par           -1.839612e-02 -1.948350e-02 -2.060198e-02 -2.174970e-02
## PctWorkMomYoungKids   -5.842245e-04 -5.987041e-04 -6.096964e-04 -6.166191e-04
## PctWorkMom            -4.411362e-03 -4.654319e-03 -4.901636e-03 -5.152751e-03
## NumIlleg               2.341768e-02  2.477748e-02  2.616549e-02  2.757680e-02
## PctIlleg               1.793680e-02  1.906884e-02  2.024476e-02  2.146428e-02
## NumImmig               1.624125e-02  1.698472e-02  1.770804e-02  1.840345e-02
## PctImmigRecent         3.031156e-03  3.118729e-03  3.196146e-03  3.262054e-03
## PctImmigRec5           4.124506e-03  4.259956e-03  4.383927e-03  4.494624e-03
## PctImmigRec8           5.357983e-03  5.574790e-03  5.783460e-03  5.982146e-03
## PctImmigRec10          6.794756e-03  7.099458e-03  7.399135e-03  7.691917e-03
## PctRecentImmig         4.520727e-03  4.712644e-03  4.897319e-03  5.072820e-03
## PctRecImmig5           4.956708e-03  5.179183e-03  5.395998e-03  5.605255e-03
## PctRecImmig8           5.206540e-03  5.454894e-03  5.699891e-03  5.939760e-03
## PctRecImmig10          5.533242e-03  5.803523e-03  6.071503e-03  6.335434e-03
## PctSpeakEnglOnly      -4.561346e-03 -4.728307e-03 -4.884015e-03 -5.026296e-03
## PctNotSpeakEnglWell    5.791866e-03  6.004597e-03  6.203928e-03  6.387316e-03
## PctLargHouseFam        9.498471e-03  9.990632e-03  1.048791e-02  1.098881e-02
## PctLargHouseOccup      7.365805e-03  7.736451e-03  8.109754e-03  8.484599e-03
## PersPerOccupHous      -9.702130e-04 -9.958350e-04 -1.015607e-03 -1.028248e-03
## PersPerOwnOccHous     -4.175748e-03 -4.418454e-03 -4.666602e-03 -4.919369e-03
## PersPerRentOccHous     6.257346e-03  6.579743e-03  6.906631e-03  7.237443e-03
## PctPersOwnOccup       -1.218042e-02 -1.273808e-02 -1.328872e-02 -1.382904e-02
## PctPersDenseHous       1.031025e-02  1.082624e-02  1.134464e-02  1.186360e-02
## PctHousLess3BR         1.248880e-02  1.304903e-02  1.359948e-02  1.413645e-02
## MedNumBR              -6.064757e-03 -6.307328e-03 -6.539836e-03 -6.759997e-03
## HousVacant             1.549826e-02  1.642826e-02  1.738258e-02  1.835866e-02
## PctHousOccup          -8.945015e-03 -9.496403e-03 -1.006799e-02 -1.065961e-02
## PctHousOwnOcc         -1.131990e-02 -1.180815e-02 -1.228431e-02 -1.274476e-02
## PctVacantBoarded       1.211178e-02  1.285396e-02  1.362146e-02  1.441358e-02
## PctVacMore6Mos         1.623904e-05 -1.774993e-05 -5.683204e-05 -1.013848e-04
## MedYrHousBuilt        -1.799603e-03 -1.839767e-03 -1.870118e-03 -1.889056e-03
## PctHousNoPhone         9.311289e-03  9.769593e-03  1.023047e-02  1.069249e-02
## PctWOFullPlumb         7.852946e-03  8.197337e-03  8.535021e-03  8.863711e-03
## OwnOccLowQuart        -3.074923e-03 -3.140288e-03 -3.195801e-03 -3.241035e-03
## OwnOccMedVal          -2.453141e-03 -2.479480e-03 -2.494315e-03 -2.497176e-03
## OwnOccHiQuart         -1.905546e-03 -1.894345e-03 -1.869735e-03 -1.831198e-03
## RentLowQ              -4.137351e-03 -4.261044e-03 -4.376036e-03 -4.481802e-03
## RentMedian            -3.663296e-03 -3.721572e-03 -3.763888e-03 -3.789188e-03
## RentHighQ             -2.884565e-03 -2.919417e-03 -2.940043e-03 -2.945515e-03
## MedRent               -3.329473e-03 -3.348974e-03 -3.348230e-03 -3.325795e-03
## MedRentPctHousInc      9.693325e-03  1.021778e-02  1.074987e-02  1.128793e-02
## MedOwnCostPctInc       2.960536e-03  3.193453e-03  3.436932e-03  3.690163e-03
## MedOwnCostPctIncNoMtg  1.208603e-03  1.244789e-03  1.274414e-03  1.295902e-03
## NumInShelters          1.963047e-02  2.074836e-02  2.188521e-02  2.303648e-02
## NumStreet              1.892904e-02  2.008067e-02  2.126593e-02  2.248254e-02
## PctForeignBorn         4.321767e-03  4.541107e-03  4.759093e-03  4.974208e-03
## PctBornSameState      -2.898224e-03 -3.117676e-03 -3.347683e-03 -3.587929e-03
## PctSameHouse85        -3.244320e-03 -3.334308e-03 -3.413254e-03 -3.479791e-03
## PctSameCity85          2.727539e-03  2.945992e-03  3.178555e-03  3.425611e-03
## PctSameState85        -5.894645e-04 -6.213398e-04 -6.529451e-04 -6.840222e-04
## LandArea               9.950952e-03  1.053664e-02  1.113404e-02  1.174064e-02
## PopDens                6.975446e-03  7.330718e-03  7.685053e-03  8.036179e-03
## PctUsePubTrans         4.379069e-03  4.670945e-03  4.971669e-03  5.279978e-03
##                                                                              
## (Intercept)            2.703442e-01  2.714944e-01  0.2726334033  2.737630e-01
## (Intercept)            .             .             .             .           
## state                 -1.207594e-04 -1.294464e-04 -0.0001385950 -1.482136e-04
## fold                  -9.263331e-05 -9.951682e-05 -0.0001069273 -1.149153e-04
## population             1.850140e-02  1.932326e-02  0.0201299555  2.091526e-02
## householdsize         -2.182387e-03 -2.314161e-03 -0.0024442013 -2.570566e-03
## racepctblack           1.966623e-02  2.094010e-02  0.0222719080  2.366219e-02
## racePctWhite          -2.031160e-02 -2.150195e-02 -0.0227325934 -2.400281e-02
## racePctAsian           1.740358e-03  1.818684e-03  0.0018914745  1.957289e-03
## racePctHisp            6.081097e-03  6.232393e-03  0.0063661558  6.480727e-03
## agePct12t21           -1.676895e-03 -2.005392e-03 -0.0023612824 -2.743859e-03
## agePct12t29            1.620337e-03  1.346574e-03  0.0010275297  6.615871e-04
## agePct16t24           -3.155778e-04 -6.011785e-04 -0.0009202682 -1.273235e-03
## agePct65up             2.376793e-03  2.491199e-03  0.0026054830  2.718958e-03
## numbUrban              1.867490e-02  1.954662e-02  0.0204091534  2.125663e-02
## pctUrban               2.978647e-03  3.247530e-03  0.0035349630  3.841512e-03
## medIncome             -8.745617e-03 -8.911633e-03 -0.0090487050 -9.154527e-03
## pctWWage              -9.094562e-03 -9.444361e-03 -0.0097858319 -1.011705e-02
## pctWFarmSelf          -6.487154e-03 -6.895718e-03 -0.0073151673 -7.743789e-03
## pctWInvInc            -1.856449e-02 -1.937389e-02 -0.0201836335 -2.099163e-02
## pctWSocSec             3.345316e-03  3.453965e-03  0.0035573353  3.654681e-03
## pctWPubAsst            1.479324e-02  1.541042e-02  0.0160202890  1.662002e-02
## pctWRetire            -3.566782e-03 -3.741108e-03 -0.0039201198 -4.104343e-03
## medFamInc             -9.927700e-03 -1.015450e-02 -0.0103538116 -1.052344e-02
## perCapInc             -6.905704e-03 -6.948508e-03 -0.0069591201 -6.936058e-03
## whitePerCap           -1.316765e-03 -1.015086e-03 -0.0006688107 -2.768840e-04
## blackPerCap           -7.098603e-03 -7.273870e-03 -0.0074349977 -7.581676e-03
## indianPerCap          -1.801832e-03 -1.802570e-03 -0.0017970971 -1.786121e-03
## AsianPerCap           -1.542287e-03 -1.379146e-03 -0.0011849602 -9.583971e-04
## OtherPerCap           -7.766099e-04 -5.784407e-04 -0.0003481699 -8.423385e-05
## HispPerCap            -3.728516e-03 -3.596220e-03 -0.0034250081 -3.213261e-03
## NumUnderPov            2.181168e-02  2.277227e-02  0.0237178221  2.464207e-02
## PctPopUnderPov         1.181612e-02  1.221930e-02  0.0126054742  1.297233e-02
## PctLess9thGrade        9.041489e-03  9.268512e-03  0.0094713229  9.647741e-03
## PctNotHSGrad           1.251320e-02  1.296010e-02  0.0133928956  1.380969e-02
## PctBSorMore           -6.793900e-03 -6.959683e-03 -0.0071089838 -7.240759e-03
## PctUnemployed          1.314146e-02  1.359685e-02  0.0140323711  1.444504e-02
## PctEmploy             -9.338654e-03 -9.617291e-03 -0.0098776419 -1.011796e-02
## PctEmplManu           -2.802079e-03 -3.032214e-03 -0.0032763922 -3.534856e-03
## PctEmplProfServ       -2.434566e-03 -2.548322e-03 -0.0026630988 -2.778651e-03
## PctOccupManu           6.173595e-03  6.274394e-03  0.0063540799  6.411683e-03
## PctOccupMgmtProf      -7.768159e-03 -7.912989e-03 -0.0080334540 -8.128416e-03
## MalePctDivorce         1.906402e-02  2.005252e-02  0.0210571230  2.207536e-02
## MalePctNevMarr         9.864459e-03  1.025294e-02  0.0106333009  1.100414e-02
## FemalePctDiv           2.057852e-02  2.160200e-02  0.0226347284  2.367315e-02
## TotalPctDiv            1.967958e-02  2.067516e-02  0.0216828199  2.269955e-02
## PersPerFam             5.375625e-03  5.629596e-03  0.0058888320  6.153731e-03
## PctFam2Par            -2.266840e-02 -2.382480e-02 -0.0250008016 -2.619443e-02
## PctKids2Par           -2.356114e-02 -2.479519e-02 -0.0260552476 -2.733982e-02
## PctYoungKids2Par      -1.949715e-02 -2.048346e-02 -0.0214865758 -2.250506e-02
## PctTeen2Par           -2.292472e-02 -2.412499e-02 -0.0253484202 -2.659291e-02
## PctWorkMomYoungKids   -6.188862e-04 -6.159211e-04 -0.0006071700 -5.921164e-04
## PctWorkMom            -5.407146e-03 -5.664382e-03 -0.0059241226 -6.186166e-03
## NumIlleg               2.900605e-02  3.044754e-02  0.0318952434  3.334291e-02
## PctIlleg               2.272712e-02  2.403301e-02  0.0253817066  2.677304e-02
## NumImmig               1.906281e-02  1.967770e-02  0.0202395055  2.073958e-02
## PctImmigRecent         3.315183e-03  3.354380e-03  0.0033786455  3.387169e-03
## PctImmigRec5           4.590317e-03  4.669380e-03  0.0047303278  4.771856e-03
## PctImmigRec8           6.169053e-03  6.342473e-03  0.0065008259  6.642694e-03
## PctImmigRec10          7.975998e-03  8.249672e-03  0.0085113810  8.759746e-03
## PctRecentImmig         5.237218e-03  5.388622e-03  0.0055252186  5.645311e-03
## PctRecImmig5           5.805049e-03  5.993517e-03  0.0061688642  6.329412e-03
## PctRecImmig8           6.172720e-03  6.397020e-03  0.0066109759  6.813005e-03
## PctRecImmig10          6.593560e-03  6.844158e-03  0.0070855738  7.316259e-03
## PctSpeakEnglOnly      -5.152994e-03 -5.262016e-03 -0.0053513738 -5.419231e-03
## PctNotSpeakEnglWell    6.552257e-03  6.696344e-03  0.0068173154  6.913110e-03
## PctLargHouseFam        1.149189e-02  1.199583e-02  0.0124994085  1.300160e-02
## PctLargHouseOccup      8.859959e-03  9.234917e-03  0.0096086912  9.980650e-03
## PersPerOccupHous      -1.032367e-03 -1.026477e-03 -0.0010090202 -9.784017e-04
## PersPerOwnOccHous     -5.175857e-03 -5.435117e-03 -0.0056961685 -5.958032e-03
## PersPerRentOccHous     7.571701e-03  7.909037e-03  0.0082491994  8.592051e-03
## PctPersOwnOccup       -1.435581e-02 -1.486594e-02 -0.0153565474 -1.582503e-02
## PctPersDenseHous       1.238142e-02  1.289654e-02  0.0134075947  1.391348e-02
## PctHousLess3BR         1.465632e-02  1.515560e-02  0.0156310464  1.607973e-02
## MedNumBR              -6.965561e-03 -7.154357e-03 -0.0073243448 -7.473650e-03
## HousVacant             1.935370e-02  2.036475e-02  0.0213887439  2.242252e-02
## PctHousOccup          -1.127113e-02 -1.190242e-02 -0.0125534179 -1.322412e-02
## PctHousOwnOcc         -1.318593e-02 -1.360440e-02 -0.0139969099 -1.436045e-02
## PctVacantBoarded       1.522954e-02  1.606850e-02  0.0169296259  1.781201e-02
## PctVacMore6Mos        -1.518037e-04 -2.085032e-04 -0.0002719163 -3.424935e-04
## MedYrHousBuilt        -1.894934e-03 -1.886079e-03 -0.0018608140 -1.817488e-03
## PctHousNoPhone         1.115436e-02  1.161486e-02  0.0120729642  1.252778e-02
## PctWOFullPlumb         9.181138e-03  9.485087e-03  0.0097734237  1.004412e-02
## OwnOccLowQuart        -3.275809e-03 -3.300224e-03 -0.0033146974 -3.319993e-03
## OwnOccMedVal          -2.487825e-03 -2.466292e-03 -0.0024328978 -2.388278e-03
## OwnOccHiQuart         -1.778441e-03 -1.711433e-03 -0.0016304218 -1.535948e-03
## RentLowQ              -4.578076e-03 -4.664882e-03 -0.0047425664 -4.811807e-03
## RentMedian            -3.796664e-03 -3.785784e-03 -0.0037563210 -3.708347e-03
## RentHighQ             -2.935103e-03 -2.908298e-03 -0.0028648202 -2.804617e-03
## MedRent               -3.280445e-03 -3.211202e-03 -0.0031173491 -2.998423e-03
## MedRentPctHousInc      1.183031e-02  1.237538e-02  0.0129215557  1.346732e-02
## MedOwnCostPctInc       3.952125e-03  4.221579e-03  0.0044970714  4.776947e-03
## MedOwnCostPctIncNoMtg  1.307492e-03  1.307225e-03  0.0012929373  1.262249e-03
## NumInShelters          2.419730e-02  2.536256e-02  0.0265269456  2.768503e-02
## NumStreet              2.372804e-02  2.499999e-02  0.0262959397  2.761355e-02
## PctForeignBorn         5.184913e-03  5.389676e-03  0.0055870142  5.775537e-03
## PctBornSameState      -3.838005e-03 -4.097409e-03 -0.0043655551 -4.641789e-03
## PctSameHouse85        -3.532639e-03 -3.570636e-03 -0.0035927553 -3.598112e-03
## PctSameCity85          3.687471e-03  3.964369e-03  0.0042564466  4.563746e-03
## PctSameState85        -7.143121e-04 -7.435618e-04 -0.0007715328 -7.980113e-04
## LandArea               1.235370e-02  1.297020e-02  0.0135869234  1.420043e-02
## PopDens                8.381739e-03  8.719338e-03  0.0090465740  9.361076e-03
## PctUsePubTrans         5.594403e-03  5.913272e-03  0.0062347200  6.556705e-03
##                                                                              
## (Intercept)            0.2748862107  0.2760074693  2.771327e-01  0.2782694544
## (Intercept)            .             .             .             .           
## state                 -0.0001583090 -0.0001688866 -1.799498e-04 -0.0001914997
## fold                  -0.0001235365 -0.0001328513 -1.429254e-04 -0.0001538298
## population             0.0216727147  0.0223956952  2.307754e-02  0.0237116601
## householdsize         -0.0026911200 -0.0028035803 -2.905567e-03 -0.0029946731
## racepctblack           0.0251112538  0.0266191189  2.818556e-02  0.0298101198
## racePctWhite          -0.0253117028 -0.0266582163 -2.804113e-02 -0.0294591271
## racePctAsian           0.0020146439  0.0020620173  2.097844e-03  0.0021204973
## racePctHisp            0.0065745855  0.0066463757  6.694959e-03  0.0067194762
## agePct12t21           -0.0031520374 -0.0035843587 -4.038980e-03 -0.0045136585
## agePct12t29            0.0002474874 -0.0002156292 -7.281676e-04 -0.0012900037
## agePct16t24           -0.0016600680 -0.0020803172 -2.533043e-03 -0.0030167594
## agePct65up             0.0028308946  0.0029405354  3.047132e-03  0.0031499976
## numbUrban              0.0220829697  0.0228819883  2.364752e-02  0.0243735513
## pctUrban               0.0041677180  0.0045140932  4.881118e-03  0.0052692295
## medIncome             -0.0092269609 -0.0092641023 -9.264385e-03 -0.0092267392
## pctWWage              -0.0104360978 -0.0107411440 -1.103051e-02 -0.0113028397
## pctWFarmSelf          -0.0081796016 -0.0086203607 -9.063562e-03 -0.0095064631
## pctWInvInc            -0.0217958947 -0.0225945669 -2.338607e-02 -0.0241692603
## pctWSocSec             0.0037453087  0.0038286230  3.904201e-03  0.0039718847
## pctWPubAsst            0.0172067704  0.0177777483  1.833033e-02  0.0188622057
## pctWRetire            -0.0042944486 -0.0044912684 -4.695800e-03 -0.0049092175
## medFamInc             -0.0106614452 -0.0107663048 -1.083702e-02 -0.0108732893
## perCapInc             -0.0068782456 -0.0067851124 -6.656726e-03 -0.0064939321
## whitePerCap            0.0001612354  0.0006454719  1.175019e-03  0.0017482272
## blackPerCap           -0.0077139983 -0.0078325456 -7.938481e-03 -0.0080336344
## indianPerCap          -0.0017706551 -0.0017520626 -1.732093e-03 -0.0017129146
## AsianPerCap           -0.0006984097 -0.0004042983 -7.577323e-05  0.0002869888
## OtherPerCap            0.0002147018  0.0005496856  9.214201e-04  0.0013302077
## HispPerCap            -0.0029598008 -0.0026639810 -2.325779e-03 -0.0019458731
## NumUnderPov            0.0255386783  0.0264014025  2.722423e-02  0.0280015369
## PctPopUnderPov         0.0133177779  0.0136400219  1.393766e-02  0.0142097444
## PctLess9thGrade        0.0097958658  0.0099141591  1.000153e-02  0.0100573853
## PctNotHSGrad           0.0142089035  0.0145893673  1.495039e-02  0.0152918288
## PctBSorMore           -0.0073542625 -0.0074491087 -7.525321e-03 -0.0075833509
## PctUnemployed          0.0148320532  0.0151908717  1.551928e-02  0.0158153859
## PctEmploy             -0.0103368217 -0.0105331546 -1.070631e-02 -0.0108560528
## PctEmplManu           -0.0038077888 -0.0040953126 -4.397484e-03 -0.0047142990
## PctEmplProfServ       -0.0028947275 -0.0030110730 -3.127427e-03 -0.0032435277
## PctOccupManu           0.0064466026  0.0064586576  6.448116e-03  0.0064156829
## PctOccupMgmtProf      -0.0081971746 -0.0082395224 -8.255760e-03 -0.0082466611
## MalePctDivorce         0.0231047247  0.0241427010  2.518683e-02  0.0262347581
## MalePctNevMarr         0.0113643022  0.0117130170  1.204991e-02  0.0123750885
## FemalePctDiv           0.0247135824  0.0257522399  2.678530e-02  0.0278089136
## TotalPctDiv            0.0237222798  0.0247478793  2.577322e-02  0.0267951434
## PersPerFam             0.0064248100  0.0067026885  6.988048e-03  0.0072815740
## PctFam2Par            -0.0274038427 -0.0286273698 -2.986355e-02 -0.0311111023
## PctKids2Par           -0.0286475787 -0.0299773678 -3.132822e-02 -0.0326992988
## PctYoungKids2Par      -0.0235376167 -0.0245830761 -2.564042e-02 -0.0267086922
## PctTeen2Par           -0.0278563785 -0.0291367582 -3.043197e-02 -0.0317398551
## PctWorkMomYoungKids   -0.0005702976 -0.0005413237 -5.048993e-04 -0.0004608511
## PctWorkMom            -0.0064504730 -0.0067171905 -6.986675e-03 -0.0072595035
## NumIlleg               0.0347841487  0.0362124915  3.762147e-02  0.0390046158
## PctIlleg               0.0282069218  0.0296833195  3.120224e-02  0.0327636516
## NumImmig               0.0211693533  0.0215204434  2.178481e-02  0.0219548210
## PctImmigRecent         0.0033793626  0.0033548909  3.313687e-03  0.0032559611
## PctImmigRec5           0.0047928693  0.0047925116  4.770173e-03  0.0047254875
## PctImmigRec8           0.0067668491  0.0068722785  6.958187e-03  0.0070239886
## PctImmigRec10          0.0089936050  0.0092120262  9.414314e-03  0.0095999897
## PctRecentImmig         0.0057473545  0.0058299885  5.892071e-03  0.0059327088
## PctRecImmig5           0.0064736289  0.0066001654  6.707889e-03  0.0067959161
## PctRecImmig8           0.0070016665  0.0071756925  7.334028e-03  0.0074758659
## PctRecImmig10          0.0075348137  0.0077400190  7.930878e-03  0.0081066547
## PctSpeakEnglOnly      -0.0054639426 -0.0054840948 -5.478535e-03 -0.0054463942
## PctNotSpeakEnglWell    0.0069819093  0.0070221780  7.032681e-03  0.0070124880
## PctLargHouseFam        0.0135015200  0.0139984771  1.449190e-02  0.0149812708
## PctLargHouseOccup      0.0103503108  0.0107173187  1.108140e-02  0.0114423083
## PersPerOccupHous      -0.0009330237 -0.0008713302 -7.918482e-04 -0.0006932203
## PersPerOwnOccHous     -0.0062197563 -0.0064804443 -6.739268e-03 -0.0069954636
## PersPerRentOccHous     0.0089375529  0.0092857301  9.636612e-03  0.0099901544
## PctPersOwnOccup       -0.0162691406 -0.0166869724 -1.707699e-02 -0.0174379380
## PctPersDenseHous       0.0144133412  0.0149066010  1.539291e-02  0.0158721022
## PctHousLess3BR         0.0164990916  0.0168869955  1.724171e-02  0.0175618494
## MedNumBR              -0.0076006027 -0.0077037544 -7.781879e-03 -0.0078339565
## HousVacant             0.0234629226  0.0245068177  2.555115e-02  0.0265929519
## PctHousOccup          -0.0139145970 -0.0146249523 -1.535533e-02 -0.0161058633
## PctHousOwnOcc         -0.0146923016 -0.0149900081 -1.525139e-02 -0.0154744429
## PctVacantBoarded       0.0187147235  0.0196367765  2.057707e-02  0.0215343090
## PctVacMore6Mos        -0.0004207048 -0.0005070481 -6.020705e-04 -0.0007064118
## MedYrHousBuilt        -0.0017544985 -0.0016703154 -1.563494e-03 -0.0014326844
## PctHousNoPhone         0.0129786028  0.0134248256  1.386590e-02  0.0143012195
## PctWOFullPlumb         0.0102952305  0.0105249187  1.073137e-02  0.0109127250
## OwnOccLowQuart        -0.0033172238 -0.0033078333 -3.293533e-03 -0.0032761913
## OwnOccMedVal          -0.0023333725 -0.0022693969 -2.197768e-03 -0.0021199882
## OwnOccHiQuart         -0.0014288273 -0.0013101128 -1.181013e-03 -0.0010427741
## RentLowQ              -0.0048736017 -0.0049292199 -4.980117e-03 -0.0050278125
## RentMedian            -0.0036422125 -0.0035584811 -3.457830e-03 -0.0033409175
## RentHighQ             -0.0027278299 -0.0026347362 -2.525664e-03 -0.0024008885
## MedRent               -0.0028541798 -0.0026845301 -2.489451e-03 -0.0022688869
## MedRentPctHousInc      0.0140112279  0.0145518735  1.508786e-02  0.0156177186
## MedOwnCostPctInc       0.0050593695  0.0053423452  5.623757e-03  0.0059013890
## MedOwnCostPctIncNoMtg  0.0012125562  0.0011410227  1.044571e-03  0.0009198800
## NumInShelters          0.0288313242  0.0299603398  3.106663e-02  0.0321448619
## NumStreet              0.0289506501  0.0303053181  3.167592e-02  0.0330611986
## PctForeignBorn         0.0059539859  0.0061212748  6.276528e-03  0.0064191094
## PctBornSameState      -0.0049254086 -0.0052156853 -5.511897e-03 -0.0058133551
## PctSameHouse85        -0.0035859623 -0.0035556870 -3.506766e-03 -0.0034387512
## PctSameCity85          0.0048861989  0.0052236121  5.575658e-03  0.0059418616
## PctSameState85        -0.0008228206 -0.0008458366 -8.670055e-04 -0.0008863617
## LandArea               0.0148071336  0.0154032867  1.598505e-02  0.0165485492
## PopDens                0.0096605402  0.0099427525  1.020562e-02  0.0104471702
## PctUsePubTrans         0.0068770336  0.0071933916  7.503384e-03  0.0078045772
##                                                                              
## (Intercept)            0.2794264218  0.2806136220  0.2818419165  2.831227e-01
## (Intercept)            .             .             .             .           
## state                 -0.0002035353 -0.0002160529 -0.0002290470 -2.425100e-04
## fold                  -0.0001656408 -0.0001784406 -0.0001923170 -2.073632e-04
## population             0.0242917349  0.0248118567  0.0252667033  2.565163e-02
## householdsize         -0.0030685318 -0.0031248858 -0.0031616274 -3.176800e-03
## racepctblack           0.0314921780  0.0332310423  0.0350260221  3.687643e-02
## racePctWhite          -0.0309108897 -0.0323951973 -0.0339110161 -3.545750e-02
## racePctAsian           0.0021282481  0.0021192223  0.0020913635  2.042447e-03
## racePctHisp            0.0067194382  0.0066948273  0.0066461939  6.574708e-03
## agePct12t21           -0.0050057226 -0.0055120330 -0.0060289585 -6.552399e-03
## agePct12t29           -0.0019003919 -0.0025578722 -0.0032602147 -4.004453e-03
## agePct16t24           -0.0035293743 -0.0040681392 -0.0046296384 -5.209847e-03
## agePct65up             0.0032485906  0.0033426253  0.0034321985  3.517893e-03
## numbUrban              0.0250544024  0.0256848435  0.0262602029  2.677638e-02
## pctUrban               0.0056787943  0.0061100828  0.0065632373  7.038257e-03
## medIncome             -0.0091508049 -0.0090371552 -0.0088874636 -8.704492e-03
## pctWWage              -0.0115572210 -0.0117934195 -0.0120119870 -1.221426e-02
## pctWFarmSelf          -0.0099461079 -0.0103793701 -0.0108029957 -1.121364e-02
## pctWInvInc            -0.0249436725 -0.0257097177 -0.0264688080 -2.722324e-02
## pctWSocSec             0.0040318939  0.0040849321  0.0041322442  4.175574e-03
## pctWPubAsst            0.0193715113  0.0198569962  0.0203180444  2.075453e-02
## pctWRetire            -0.0051328771 -0.0053683112 -0.0056172117 -5.881400e-03
## medFamInc             -0.0108757188 -0.0108458972 -0.0107863746 -1.070038e-02
## perCapInc             -0.0062984843 -0.0060730860 -0.0058212782 -5.547104e-03
## whitePerCap            0.0023625277  0.0030144455  0.0036997618  4.413859e-03
## blackPerCap           -0.0081205545 -0.0082024618 -0.0082830670 -8.366226e-03
## indianPerCap          -0.0016971220 -0.0016877003 -0.0016879264 -1.701206e-03
## AsianPerCap            0.0006833152  0.0011120611  0.0015717100  2.060557e-03
## OtherPerCap            0.0017759222  0.0022580304  0.0027756904  3.327929e-03
## HispPerCap            -0.0015256695 -0.0010672467 -0.0005731725 -4.620515e-05
## NumUnderPov            0.0287282613  0.0293999291  0.0300126141  3.056274e-02
## PctPopUnderPov         0.0144557943  0.0146757038  0.0148694975  1.503694e-02
## PctLess9thGrade        0.0100816711  0.0100747707  0.0100373202  9.969899e-03
## PctNotHSGrad           0.0156140713  0.0159179306  0.0162043916  1.647426e-02
## PctBSorMore           -0.0076240366 -0.0076484757 -0.0076578029 -7.652915e-03
## PctUnemployed          0.0160775981  0.0163044360  0.0164942757  1.664502e-02
## PctEmploy             -0.0109824416 -0.0110856667 -0.0111657404 -1.122218e-02
## PctEmplManu           -0.0050456933 -0.0053915534 -0.0057517220 -6.126001e-03
## PctEmplProfServ       -0.0033591161 -0.0034739548 -0.0035878527 -3.700700e-03
## PctOccupManu           0.0063624220  0.0062895960  0.0061984412  6.089943e-03
## PctOccupMgmtProf      -0.0082133527 -0.0081571145 -0.0080791183 -7.980206e-03
## MalePctDivorce         0.0272841933  0.0283328888  0.0293784926  3.041838e-02
## MalePctNevMarr         0.0126891253  0.0129930517  0.0132882542  1.357635e-02
## FemalePctDiv           0.0288191380  0.0298118744  0.0307826831  3.172660e-02
## TotalPctDiv            0.0278104530  0.0288157723  0.0298074069  3.078119e-02
## PersPerFam             0.0075838792  0.0078954061  0.0082163454  8.546598e-03
## PctFam2Par            -0.0323688264 -0.0336354211 -0.0349092355 -3.618804e-02
## PctKids2Par           -0.0340897819 -0.0354986571 -0.0369244895 -3.836524e-02
## PctYoungKids2Par      -0.0277868568 -0.0288736121 -0.0299671776 -3.106517e-02
## PctTeen2Par           -0.0330580131 -0.0343836478 -0.0357133781 -3.704315e-02
## PctWorkMomYoungKids   -0.0004091614 -0.0003500095 -0.0002838185 -2.113050e-04
## PctWorkMom            -0.0075364731 -0.0078185987 -0.0081071101 -8.403472e-03
## NumIlleg               0.0403554776  0.0416675287  0.0429341235  4.414849e-02
## PctIlleg               0.0343673521  0.0360128481  0.0376992290  3.942514e-02
## NumImmig               0.0220233877  0.0219840031  0.0218308673  2.155904e-02
## PctImmigRecent         0.0031821834  0.0030930518  0.0029894390  2.872342e-03
## PctImmigRec5           0.0046583032  0.0045686377  0.0044566224  4.322460e-03
## PctImmigRec8           0.0070692757  0.0070937723  0.0070972853  7.079673e-03
## PctImmigRec10          0.0097687492  0.0099204094  0.0100548550  1.017202e-02
## PctRecentImmig         0.0059512940  0.0059475498  0.0059215912  5.873999e-03
## PctRecImmig5           0.0068636520  0.0069108376  0.0069376126  6.944584e-03
## PctRecImmig8           0.0076006955  0.0077083549  0.0077990968  7.873652e-03
## PctRecImmig10          0.0082669194  0.0084116003  0.0085410423  8.656055e-03
## PctSpeakEnglOnly      -0.0053870972 -0.0053003706 -0.0051862498 -5.045095e-03
## PctNotSpeakEnglWell    0.0069609584  0.0068777142  0.0067626205  6.615790e-03
## PctLargHouseFam        0.0154660649  0.0159455981  0.0164189546  1.688495e-02
## PctLargHouseOccup      0.0117996955  0.0121530531  0.0125016261  1.284441e-02
## PersPerOccupHous      -0.0005742196 -0.0004337414 -0.0002707745 -8.437267e-05
## PersPerOwnOccHous     -0.0072482982 -0.0074970219 -0.0077408158 -7.978783e-03
## PersPerRentOccHous     0.0103461447  0.0107041133  0.0110632777  1.142255e-02
## PctPersOwnOccup       -0.0177687733 -0.0180685133 -0.0183361643 -1.857073e-02
## PctPersDenseHous       0.0163441260  0.0168089775  0.0172666804  1.771733e-02
## PctHousLess3BR         0.0178463361  0.0180942732  0.0183049385  1.847786e-02
## MedNumBR              -0.0078591382 -0.0078567237 -0.0078261628 -7.767115e-03
## HousVacant             0.0276293914  0.0286577744  0.0296756133  3.068073e-02
## PctHousOccup          -0.0168765754 -0.0176673457 -0.0184778474 -1.930757e-02
## PctHousOwnOcc         -0.0156573129 -0.0157981918 -0.0158953537 -1.594727e-02
## PctVacantBoarded       0.0225069637  0.0234931454  0.0244905953  2.549673e-02
## PctVacMore6Mos        -0.0008208743 -0.0009465153 -0.0010847396 -1.237348e-03
## MedYrHousBuilt        -0.0012766441 -0.0010942528 -0.0008845596 -6.468674e-04
## PctHousNoPhone         0.0147299368  0.0151508571  0.0155623349  1.596232e-02
## PctWOFullPlumb         0.0110670385  0.0111921842  0.0112858842  1.134581e-02
## OwnOccLowQuart        -0.0032576709 -0.0032396433 -0.0032234323 -3.209975e-03
## OwnOccMedVal          -0.0020374904 -0.0019514775 -0.0018628122 -1.772037e-03
## OwnOccHiQuart         -0.0008965427 -0.0007432302 -0.0005834567 -4.176276e-04
## RentLowQ              -0.0050737481 -0.0051191787 -0.0051651555 -5.212672e-03
## RentMedian            -0.0032082516 -0.0030601027 -0.0028965377 -2.717622e-03
## RentHighQ             -0.0022605438 -0.0021045857 -0.0019328666 -1.745344e-03
## MedRent               -0.0020226833 -0.0017505903 -0.0014524001 -1.128220e-03
## MedRentPctHousInc      0.0161399001  0.0166527108  0.0171543858  1.764322e-02
## MedOwnCostPctInc       0.0061729347  0.0064359758  0.0066879205  6.925912e-03
## MedOwnCostPctIncNoMtg  0.0007633990  0.0005713791  0.0003399416  6.518006e-05
## NumInShelters          0.0331898741  0.0341968375  0.0351614561  3.608024e-02
## NumStreet              0.0344603310  0.0358730732  0.0372999173  3.874230e-02
## PctForeignBorn         0.0065486361  0.0066649836  0.0067682677  6.858812e-03
## PctBornSameState      -0.0061194208 -0.0064295022 -0.0067430071 -7.059253e-03
## PctSameHouse85        -0.0033512415 -0.0032438918 -0.0031164496 -2.968823e-03
## PctSameCity85          0.0063216020  0.0067141269  0.0071186023  7.534194e-03
## PctSameState85        -0.0009040400 -0.0009202713 -0.0009353491 -9.495594e-04
## LandArea               0.0170899004  0.0176053504  0.0180913745  1.854481e-02
## PopDens                0.0106656154  0.0108593568  0.0110270666  1.116777e-02
## PctUsePubTrans         0.0080945478  0.0083709128  0.0086313455  8.873556e-03
##                                                                              
## (Intercept)            0.2844678051  2.858891e-01  2.873988e-01  0.2890094069
## (Intercept)            .             .             .             .           
## state                 -0.0002564333 -2.708074e-04 -2.856229e-04 -0.0003008692
## fold                  -0.0002236762 -2.413540e-04 -2.604924e-04 -0.0002811811
## population             0.0259626703  2.619646e-02  2.635017e-02  0.0264214815
## householdsize         -0.0031685535 -3.135073e-03 -3.074532e-03 -0.0029851269
## racepctblack           0.0387814790  4.074002e-02  4.275032e-02  0.0448099275
## racePctWhite          -0.0370338634 -3.863913e-02 -4.027189e-02 -0.0419301238
## racePctAsian           0.0019701783  1.872379e-03  1.747211e-03  0.0015933171
## racePctHisp            0.0064821307  6.370687e-03  6.242887e-03  0.0061013278
## agePct12t21           -0.0070778920 -7.600805e-03 -8.116568e-03 -0.0086208596
## agePct12t29           -0.0047870414 -5.604132e-03 -6.451867e-03 -0.0073266097
## agePct16t24           -0.0058042778 -6.408189e-03 -7.016803e-03 -0.0076254545
## agePct65up             0.0036008102  3.682491e-03  3.764756e-03  0.0038495470
## numbUrban              0.0272297807  2.761723e-02  2.793591e-02  0.0281834306
## pctUrban               0.0075350187  8.053332e-03  8.593022e-03  0.0091539847
## medIncome             -0.0084918057 -8.253239e-03 -7.992289e-03 -0.0077117083
## pctWWage              -0.0124021699 -1.257782e-02 -1.274311e-02 -0.0128994698
## pctWFarmSelf          -0.0116079142 -1.198235e-02 -1.233342e-02 -0.0126575428
## pctWInvInc            -0.0279757869 -2.872903e-02 -2.948476e-02 -0.0302435850
## pctWSocSec             0.0042169872  4.258599e-03  4.302315e-03  0.0043497257
## pctWPubAsst            0.0211664518  2.155345e-02  2.191441e-02  0.0222472907
## pctWRetire            -0.0061627923 -6.463384e-03 -6.785252e-03 -0.0071305713
## medFamInc             -0.0105912663 -1.046192e-02 -1.031423e-02 -0.0101490407
## perCapInc             -0.0052545764 -4.947106e-03 -4.627140e-03 -0.0042961672
## whitePerCap            0.0051521925  5.910735e-03  6.686180e-03  0.0074758034
## blackPerCap           -0.0084554964 -8.553758e-03 -8.663075e-03 -0.0087848839
## indianPerCap          -0.0017308749 -1.780036e-03 -1.851496e-03 -0.0019478270
## AsianPerCap            0.0025769310  3.119365e-03  3.686626e-03  0.0042775790
## OtherPerCap            0.0039138550  4.532828e-03  5.184475e-03  0.0058685424
## HispPerCap             0.0005110487  1.096369e-03  1.707919e-03  0.0023439805
## NumUnderPov            0.0310468337  3.146131e-02  3.180255e-02  0.0320671182
## PctPopUnderPov         0.0151771255  1.528815e-02  1.536719e-02  0.0154108159
## PctLess9thGrade        0.0098726988  9.745322e-03  9.586830e-03  0.0093960257
## PctNotHSGrad           0.0167278060  1.696463e-02  1.718378e-02  0.0173841861
## PctBSorMore           -0.0076342418 -7.601703e-03 -7.554908e-03 -0.0074935366
## PctUnemployed          0.0167538596  1.681724e-02  1.683118e-02  0.0167917239
## PctEmploy             -0.0112538314 -1.125888e-02 -1.123526e-02 -0.0111811059
## PctEmplManu           -0.0065141472 -6.915855e-03 -7.330726e-03 -0.0077582290
## PctEmplProfServ       -0.0038125032 -3.923404e-03 -4.033672e-03 -0.0041436732
## PctOccupManu           0.0059647238  5.823157e-03  5.665703e-03  0.0054933413
## PctOccupMgmtProf      -0.0078608347 -7.721286e-03 -7.562102e-03 -0.0073845451
## MalePctDivorce         0.0314495377  3.246855e-02  3.347185e-02  0.0344560532
## MalePctNevMarr         0.0138590941  1.413835e-02  1.441621e-02  0.0146951339
## FemalePctDiv           0.0326380125  3.351072e-02  3.433818e-02  0.0351138771
## TotalPctDiv            0.0317323964  3.265589e-02  3.354638e-02  0.0343987399
## PersPerFam             0.0088858210  9.233546e-03  9.589315e-03  0.0099527132
## PctFam2Par            -0.0374689561 -3.874862e-02 -4.002358e-02 -0.0412907654
## PctKids2Par           -0.0398182682 -4.128056e-02 -4.274916e-02 -0.0442215211
## PctYoungKids2Par      -0.0321646337 -3.326235e-02 -3.435520e-02 -0.0354404531
## PctTeen2Par           -0.0383683291 -3.968395e-02 -4.098504e-02 -0.0422668683
## PctWorkMomYoungKids   -0.0001335198 -5.187038e-05  3.188853e-05  0.0001156996
## PctWorkMom            -0.0087094363 -9.027122e-03 -9.359080e-03 -0.0097082943
## NumIlleg               0.0453038853  4.639380e-02  4.741231e-02  0.0483541097
## PctIlleg               0.0411888863  4.298867e-02  4.482279e-02  0.0466897803
## NumImmig               0.0211646800  2.064525e-02  1.999968e-02  0.0192281376
## PctImmigRecent         0.0027428536  2.602182e-03  2.451693e-03  0.0022929505
## PctImmigRec5           0.0041664238  3.988892e-03  3.790406e-03  0.0035716825
## PctImmigRec8           0.0070408535  6.980841e-03  6.899782e-03  0.0067979422
## PctImmigRec10          0.0102718951  1.035462e-02  1.042051e-02  0.0104700086
## PctRecentImmig         0.0058058839  5.718909e-03  5.615218e-03  0.0054972548
## PctRecImmig5           0.0069328808  6.904147e-03  6.860444e-03  0.0068040533
## PctRecImmig8           0.0079332640  7.979648e-03  8.014867e-03  0.0080411209
## PctRecImmig10          0.0087579281  8.848371e-03  8.929374e-03  0.0090030177
## PctSpeakEnglOnly      -0.0048776116 -4.684855e-03 -4.468189e-03 -0.0042291833
## PctNotSpeakEnglWell    0.0064376162  6.228814e-03  5.990416e-03  0.0057237121
## PctLargHouseFam        0.0173421917  1.778917e-02  1.822436e-02  0.0186462321
## PctLargHouseOccup      0.0131801996  1.350770e-02  1.382559e-02  0.0141324677
## PersPerOccupHous       0.0001263412  3.621167e-04  6.234807e-04  0.0009106751
## PersPerOwnOccHous     -0.0082100190 -8.433763e-03 -8.649571e-03 -0.0088573972
## PersPerRentOccHous     0.0117805993  1.213598e-02  1.248719e-02  0.0128326963
## PctPersOwnOccup       -0.0187713593 -1.893759e-02 -1.906954e-02 -0.0191679858
## PctPersDenseHous       0.0181612277  1.859897e-02  1.903155e-02  0.0194602874
## PctHousLess3BR         0.0186130507  1.871124e-02  1.877412e-02  0.0188042973
## MedNumBR              -0.0076795601 -7.563923e-03 -7.421141e-03 -0.0072526430
## HousVacant             0.0316713773  3.264641e-02  3.360530e-02  0.0345481533
## PctHousOccup          -0.0201559031 -2.102232e-02 -2.190649e-02 -0.0228083673
## PctHousOwnOcc         -0.0159528462 -1.591163e-02 -1.582396e-02 -0.0156909431
## PctVacantBoarded       0.0265087611  2.752392e-02  2.853960e-02  0.0295534017
## PctVacMore6Mos        -0.0014064907 -1.594498e-03 -1.803648e-03 -0.0020359708
## MedYrHousBuilt        -0.0003808545 -8.670392e-05  2.348052e-04  0.0005822754
## PctHousNoPhone         0.0163485756  1.671896e-02  1.707173e-02  0.0174056856
## PctWOFullPlumb         0.0113698108  1.135610e-02  1.130349e-02  0.0112113643
## OwnOccLowQuart        -0.0031999645 -3.194173e-03 -3.193819e-03 -0.0032008027
## OwnOccMedVal          -0.0016795710 -1.586044e-03 -1.492629e-03 -0.0014011945
## OwnOccHiQuart         -0.0002461763 -6.990262e-05  1.097460e-04  0.0002906080
## RentLowQ              -0.0052629772 -5.317965e-03 -5.380450e-03 -0.0054541864
## RentMedian            -0.0025237704 -2.316123e-03 -2.096739e-03 -0.0018684924
## RentHighQ             -0.0015423835 -1.325029e-03 -1.095085e-03 -0.0008549287
## MedRent               -0.0007788084 -4.058217e-04 -1.179573e-05  0.0004001606
## MedRentPctHousInc      0.0181177894  1.857709e-02  1.902065e-02  0.0194484308
## MedOwnCostPctInc       0.0071467472  7.346855e-03  7.522398e-03  0.0076694747
## MedOwnCostPctIncNoMtg -0.0002567166 -6.293534e-04 -1.056064e-03 -0.0015398994
## NumInShelters          0.0369507910  3.777191e-02  3.854358e-02  0.0392667764
## NumStreet              0.0402027561  4.168504e-02  4.319401e-02  0.0447355180
## PctForeignBorn         0.0069371102  7.003806e-03  7.059727e-03  0.0071059898
## PctBornSameState      -0.0073773481 -7.696091e-03 -8.013948e-03 -0.0083291446
## PctSameHouse85        -0.0028011425 -2.613777e-03 -2.407272e-03 -0.0021822599
## PctSameCity85          0.0079601727  8.395998e-03  8.841346e-03  0.0092960510
## PctSameState85        -0.0009630852 -9.759220e-04 -9.878536e-04 -0.0009985115
## LandArea               0.0189629886  1.934376e-02  1.968552e-02  0.0199871527
## PopDens                0.0112808988  1.136638e-02  1.142458e-02  0.0114563461
## PctUsePubTrans         0.0090952450  9.294061e-03  9.467600e-03  0.0096134766
##                                                                              
## (Intercept)            0.2906669927  0.2925249693  0.2945364044  2.967110e-01
## (Intercept)            .             .             .             .           
## state                 -0.0003170709 -0.0003331857 -0.0003496599 -3.664867e-04
## fold                  -0.0003040242 -0.0003279517 -0.0003536785 -3.812063e-04
## population             0.0264978220  0.0263902693  0.0262054263  2.593245e-02
## householdsize         -0.0028628782 -0.0027025172 -0.0025160008 -2.295949e-03
## racepctblack           0.0469924846  0.0491226748  0.0513048655  5.352072e-02
## racePctWhite          -0.0436895858 -0.0453652152 -0.0470704802 -4.878594e-02
## racePctAsian           0.0014119174  0.0012105366  0.0009689244  6.933258e-04
## racePctHisp            0.0059965497  0.0058161680  0.0056353656  5.449573e-03
## agePct12t21           -0.0090717801 -0.0095569769 -0.0100153054 -1.044952e-02
## agePct12t29           -0.0081854891 -0.0091351832 -0.0100922057 -1.106713e-02
## agePct16t24           -0.0082216114 -0.0088373959 -0.0094326640 -1.001170e-02
## agePct65up             0.0039916409  0.0040764410  0.0041735982  4.281637e-03
## numbUrban              0.0283886721  0.0284706608  0.0284862969  2.842346e-02
## pctUrban               0.0097271500  0.0103362929  0.0109636955  1.161141e-02
## medIncome             -0.0074751411 -0.0071086256 -0.0067555246 -6.390490e-03
## pctWWage              -0.0130986085 -0.0132034453 -0.0133249985 -1.344281e-02
## pctWFarmSelf          -0.0129783243 -0.0132341585 -0.0134544685 -1.363258e-02
## pctWInvInc            -0.0310527447 -0.0317580291 -0.0325020179 -3.324820e-02
## pctWSocSec             0.0044101266  0.0044479039  0.0045099464  4.583087e-03
## pctWPubAsst            0.0225679268  0.0227970452  0.0230184568  2.320143e-02
## pctWRetire            -0.0074932156 -0.0078876156 -0.0083132208 -8.770901e-03
## medFamInc             -0.0099250705 -0.0096825653 -0.0094567021 -9.216308e-03
## perCapInc             -0.0038951299 -0.0035172085 -0.0031605894 -2.801051e-03
## whitePerCap            0.0083405578  0.0091605596  0.0099642651  1.076589e-02
## blackPerCap           -0.0088668667 -0.0090091471 -0.0091855705 -9.381330e-03
## indianPerCap          -0.0020455927 -0.0021974418 -0.0023877111 -2.613014e-03
## AsianPerCap            0.0049187367  0.0055563766  0.0062047945  6.869674e-03
## OtherPerCap            0.0066123170  0.0073622221  0.0081342848  8.935078e-03
## HispPerCap             0.0030492956  0.0037290421  0.0044151437  5.115024e-03
## NumUnderPov            0.0322076813  0.0322941556  0.0323087778  3.223664e-02
## PctPopUnderPov         0.0153488175  0.0153080510  0.0152339701  1.510995e-02
## PctLess9thGrade        0.0091172184  0.0088576061  0.0085712635  8.248766e-03
## PctNotHSGrad           0.0175004245  0.0176657609  0.0178174185  1.794854e-02
## PctBSorMore           -0.0073712701 -0.0072948989 -0.0072063962 -7.106118e-03
## PctUnemployed          0.0166309098  0.0164846470  0.0162741186  1.599625e-02
## PctEmploy             -0.0110172838 -0.0109150560 -0.0107714160 -1.059102e-02
## PctEmplManu           -0.0081893182 -0.0086361962 -0.0090921970 -9.556844e-03
## PctEmplProfServ       -0.0042828819 -0.0043922509 -0.0045048266 -4.619354e-03
## PctOccupManu           0.0052560519  0.0050752687  0.0048798886  4.678050e-03
## PctOccupMgmtProf      -0.0071327458 -0.0069450755 -0.0067343743 -6.514803e-03
## MalePctDivorce         0.0353909662  0.0363238606  0.0372344507  3.811458e-02
## MalePctNevMarr         0.0149431395  0.0152275435  0.0155207282  1.582576e-02
## FemalePctDiv           0.0357799214  0.0364287344  0.0370072964  3.750729e-02
## TotalPctDiv            0.0351396673  0.0358990145  0.0366040573  3.725172e-02
## PersPerFam             0.0103160281  0.0107022752  0.0110845972  1.147172e-02
## PctFam2Par            -0.0424401797 -0.0436889792 -0.0449134376 -4.611928e-02
## PctKids2Par           -0.0455885530 -0.0470721703 -0.0485409613 -5.000589e-02
## PctYoungKids2Par      -0.0364249897 -0.0375039610 -0.0385553277 -3.959156e-02
## PctTeen2Par           -0.0434455771 -0.0446902855 -0.0458892511 -4.705183e-02
## PctWorkMomYoungKids    0.0001791319  0.0002591977  0.0003325582  3.965407e-04
## PctWorkMom            -0.0100713219 -0.0104663915 -0.0108850591 -1.133572e-02
## NumIlleg               0.0491267349  0.0499089452  0.0505895297  5.117878e-02
## PctIlleg               0.0485411364  0.0504849481  0.0524469844  5.443841e-02
## NumImmig               0.0183023668  0.0172820068  0.0161283493  1.485850e-02
## PctImmigRecent         0.0021009860  0.0019317322  0.0017574925  1.582035e-03
## PctImmigRec5           0.0033079932  0.0030539930  0.0027788997  2.487161e-03
## PctImmigRec8           0.0066539191  0.0065150651  0.0063530104  6.172008e-03
## PctImmigRec10          0.0104831475  0.0105079983  0.0105136498  1.050629e-02
## PctRecentImmig         0.0053841600  0.0052400318  0.0050871314  4.930552e-03
## PctRecImmig5           0.0067500927  0.0066693363  0.0065824055  6.493235e-03
## PctRecImmig8           0.0080704632  0.0080786687  0.0080861354  8.095256e-03
## PctRecImmig10          0.0090759088  0.0091350528  0.0091958199  9.259789e-03
## PctSpeakEnglOnly      -0.0039580130 -0.0036798249 -0.0033836595 -3.074787e-03
## PctNotSpeakEnglWell    0.0054126773  0.0050995758  0.0047597164  4.400371e-03
## PctLargHouseFam        0.0190337790  0.0194397380  0.0198196405  2.018136e-02
## PctLargHouseOccup      0.0144261430  0.0147193264  0.0149898748  1.524347e-02
## PersPerOccupHous       0.0012614834  0.0015943332  0.0019546497  2.337927e-03
## PersPerOwnOccHous     -0.0090318965 -0.0092392968 -0.0094341396 -9.625810e-03
## PersPerRentOccHous     0.0131684115  0.0135034249  0.0138216353  1.412663e-02
## PctPersOwnOccup       -0.0192015150 -0.0192628634 -0.0192848137 -1.928485e-02
## PctPersDenseHous       0.0198859079  0.0203230902  0.0207555455  2.119227e-02
## PctHousLess3BR         0.0187999425  0.0187997408  0.0187669270  1.872027e-02
## MedNumBR              -0.0070646366 -0.0068610944 -0.0066333352 -6.389443e-03
## HousVacant             0.0354607526  0.0363837036  0.0372913501  3.819176e-02
## PctHousOccup          -0.0237159600 -0.0246653561 -0.0256273597 -2.660897e-02
## PctHousOwnOcc         -0.0155444421 -0.0153498238 -0.0151079650 -1.483164e-02
## PctVacantBoarded       0.0305592740  0.0315769581  0.0325799774  3.357355e-02
## PctVacMore6Mos        -0.0023256205 -0.0025976218 -0.0029056345 -3.243296e-03
## MedYrHousBuilt         0.0009388308  0.0013256166  0.0017345867  2.159047e-03
## PctHousNoPhone         0.0177240460  0.0180442035  0.0183300837  1.859499e-02
## PctWOFullPlumb         0.0110999865  0.0109433831  0.0107392259  1.049636e-02
## OwnOccLowQuart        -0.0031929991 -0.0032527667 -0.0033100504 -3.385192e-03
## OwnOccMedVal          -0.0013162855 -0.0012638974 -0.0012033945 -1.154296e-03
## OwnOccHiQuart          0.0004454164  0.0005975708  0.0007593472  9.125446e-04
## RentLowQ              -0.0055895279 -0.0057235182 -0.0058650429 -6.035404e-03
## RentMedian            -0.0017017999 -0.0014841212 -0.0012513581 -1.020910e-03
## RentHighQ             -0.0006741667 -0.0004292069 -0.0001711327  8.934335e-05
## MedRent                0.0007457799  0.0011830970  0.0016371062  2.099457e-03
## MedRentPctHousInc      0.0199147900  0.0203246505  0.0207146261  2.109172e-02
## MedOwnCostPctInc       0.0077636432  0.0078495915  0.0078971731  7.903813e-03
## MedOwnCostPctIncNoMtg -0.0020637751 -0.0026715520 -0.0033448700 -4.085357e-03
## NumInShelters          0.0399907419  0.0406287092  0.0412267890  4.179013e-02
## NumStreet              0.0463526060  0.0479843270  0.0496716036  5.142300e-02
## PctForeignBorn         0.0071421187  0.0071799390  0.0072138872  7.245521e-03
## PctBornSameState      -0.0086254338 -0.0089318060 -0.0092335816 -9.525552e-03
## PctSameHouse85        -0.0019809774 -0.0017241476 -0.0014516970 -1.163126e-03
## PctSameCity85          0.0097684651  0.0102403696  0.0107195413  1.120779e-02
## PctSameState85        -0.0009978908 -0.0010041094 -0.0010100020 -1.012271e-03
## LandArea               0.0202597908  0.0204791495  0.0206593903  2.080099e-02
## PopDens                0.0114921905  0.0114784124  0.0114432010  1.138734e-02
## PctUsePubTrans         0.0097242382  0.0098088614  0.0098630271  9.881801e-03
##                                                                              
## (Intercept)            0.2990614428  3.015985e-01  0.3043314413  0.3072695046
## (Intercept)            .             .             .             .           
## state                 -0.0003836394 -4.010964e-04 -0.0004188279 -0.0004367998
## fold                  -0.0004105552 -4.417335e-04 -0.0004747228 -0.0005094824
## population             0.0255732481  2.512928e-02  0.0246022578  0.0239938802
## householdsize         -0.0020423780 -1.754831e-03 -0.0014336772 -0.0010797448
## racepctblack           0.0557668363  5.803885e-02  0.0603324972  0.0626437736
## racePctWhite          -0.0505095471 -5.223723e-02 -0.0539646042 -0.0556869213
## racePctAsian           0.0003837140  4.006756e-05 -0.0003376649 -0.0007499803
## racePctHisp            0.0052624702  5.076462e-03  0.0048931334  0.0047135271
## agePct12t21           -0.0108568619 -1.123469e-02 -0.0115810633 -0.0118944292
## agePct12t29           -0.0120562429 -1.305792e-02 -0.0140716224 -0.0150980000
## agePct16t24           -0.0105695465 -1.110288e-02 -0.0116090327 -0.0120861575
## agePct65up             0.0044021582  4.538221e-03  0.0046924172  0.0048674743
## numbUrban              0.0282846362  2.806991e-02  0.0277798423  0.0274147002
## pctUrban               0.0122793053  1.296687e-02  0.0136736067  0.0143987655
## medIncome             -0.0060168839 -5.635371e-03 -0.0052458683 -0.0048480482
## pctWWage              -0.0135599903 -1.367761e-02 -0.0137967119 -0.0139185398
## pctWFarmSelf          -0.0137652330 -1.384911e-02 -0.0138810240 -0.0138580032
## pctWInvInc            -0.0339997415 -3.475442e-02 -0.0355103075 -0.0362658412
## pctWSocSec             0.0046703902  4.773043e-03  0.0048924851  0.0050302438
## pctWPubAsst            0.0233468746  2.345060e-02  0.0235093470  0.0235202613
## pctWRetire            -0.0092643335 -9.796090e-03 -0.0103689770 -0.0109856521
## medFamInc             -0.0089657322 -8.702628e-03 -0.0084259858 -0.0081349675
## perCapInc             -0.0024436079 -2.087265e-03 -0.0017328451 -0.0013817782
## whitePerCap            0.0115588650  1.234104e-02  0.0131080592  0.0138545494
## blackPerCap           -0.0096004394 -9.842358e-03 -0.0101082251 -0.0103992909
## indianPerCap          -0.0028762334 -3.178347e-03 -0.0035206805 -0.0039042487
## AsianPerCap            0.0075473388  8.236165e-03  0.0089334203  0.0096360854
## OtherPerCap            0.0097618157  1.061370e-02  0.0114887564  0.0123847140
## HispPerCap             0.0058230263  6.536853e-03  0.0072524374  0.0079654386
## NumUnderPov            0.0320812118  3.184133e-02  0.0315178045  0.0311115234
## PctPopUnderPov         0.0149361200  1.470748e-02  0.0144213259  0.0140749718
## PctLess9thGrade        0.0078922725  7.499926e-03  0.0070715029  0.0066066941
## PctNotHSGrad           0.0180623226  1.815823e-02  0.0182379327  0.0183032232
## PctBSorMore           -0.0069966055 -6.879038e-03 -0.0067562974 -0.0066316264
## PctUnemployed          0.0156509027  1.523562e-02  0.0147498845  0.0141932738
## PctEmploy             -0.0103742517 -1.012014e-02 -0.0098288583 -0.0094998384
## PctEmplManu           -0.0100289935 -1.050715e-02 -0.0109896471 -0.0114746177
## PctEmplProfServ       -0.0047363503 -4.856511e-03 -0.0049802651 -0.0051080907
## PctOccupManu           0.0044741439  4.272264e-03  0.0040777513  0.0038958289
## PctOccupMgmtProf      -0.0062901648 -6.064868e-03 -0.0058438840 -0.0056315843
## MalePctDivorce         0.0389658007  3.978782e-02  0.0405820528  0.0413501143
## MalePctNevMarr         0.0161479987  1.649100e-02  0.0168586785  0.0172542741
## FemalePctDiv           0.0379255161  3.825677e-02  0.0384969084  0.0386414710
## TotalPctDiv            0.0378401002  3.836594e-02  0.0388267637  0.0392197062
## PersPerFam             0.0118618574  1.225399e-02  0.0126467778  0.0130389431
## PctFam2Par            -0.0473062770 -4.847294e-02 -0.0496183344 -0.0507406319
## PctKids2Par           -0.0514658767 -5.291972e-02 -0.0543661820 -0.0558032869
## PctYoungKids2Par      -0.0406101988 -4.160963e-02 -0.0425880117 -0.0435430665
## PctTeen2Par           -0.0481720213 -4.924475e-02 -0.0502643614 -0.0512248612
## PctWorkMomYoungKids    0.0004480867  4.841411e-04  0.0005019260  0.0004988708
## PctWorkMom            -0.0118223837 -1.234945e-02 -0.0129210785 -0.0135413545
## NumIlleg               0.0516722571  5.206634e-02  0.0523569364  0.0525398661
## PctIlleg               0.0564560659  5.849831e-02  0.0605629667  0.0626479660
## NumImmig               0.0134744616  1.197978e-02  0.0103767545  0.0086675100
## PctImmigRecent         0.0014077715  1.236618e-03  0.0010705887  0.0009115052
## PctImmigRec5           0.0021796620  1.857057e-03  0.0015198375  0.0011682461
## PctImmigRec8           0.0059719933  5.752837e-03  0.0055141892  0.0052555866
## PctImmigRec10          0.0104857117  1.045210e-02  0.0104054003  0.0103456489
## PctRecentImmig         0.0047724509  4.615106e-03  0.0044599554  0.0043081704
## PctRecImmig5           0.0064035870  6.315426e-03  0.0062300555  0.0061488030
## PctRecImmig8           0.0081077282  8.125488e-03  0.0081501015  0.0081834516
## PctRecImmig10          0.0093285589  9.404026e-03  0.0094879573  0.0095825815
## PctSpeakEnglOnly      -0.0027543931 -2.424555e-03 -0.0020872934 -0.0017450540
## PctNotSpeakEnglWell    0.0040218940  3.626063e-03  0.0032145275  0.0027892708
## PctLargHouseFam        0.0205202739  2.083401e-02  0.0211199772  0.0213760626
## PctLargHouseOccup      0.0154756323  1.568425e-02  0.0158669252  0.0160216188
## PersPerOccupHous       0.0027433240  3.170849e-03  0.0036207455  0.0040935807
## PersPerOwnOccHous     -0.0098145896 -1.000144e-02 -0.0101872038 -0.0103729911
## PersPerRentOccHous     0.0144146473  1.468390e-02  0.0149319779  0.0151563800
## PctPersOwnOccup       -0.0192625869 -1.922118e-02 -0.0191636715 -0.0190940221
## PctPersDenseHous       0.0216331640  2.208071e-02  0.0225369451  0.0230040624
## PctHousLess3BR         0.0186615516  1.859637e-02  0.0185297966  0.0184674182
## MedNumBR              -0.0061306023 -5.859758e-03 -0.0055796323 -0.0052931806
## HousVacant             0.0390860590  3.997758e-02  0.0408700950  0.0417686040
## PctHousOccup          -0.0276097452 -2.863042e-02 -0.0296715900 -0.0307339728
## PctHousOwnOcc         -0.0145211629 -1.418085e-02 -0.0138149537 -0.0134287140
## PctVacantBoarded       0.0345543826  3.552113e-02  0.0364723633  0.0374070935
## PctVacMore6Mos        -0.0036121227 -4.013038e-03 -0.0044469040 -0.0049147534
## MedYrHousBuilt         0.0025960191  3.041003e-03  0.0034893824  0.0039361442
## PctHousNoPhone         0.0188363229  1.905474e-02  0.0192505197  0.0194241683
## PctWOFullPlumb         0.0102141375  9.894695e-03  0.0095398990  0.0091518901
## OwnOccLowQuart        -0.0034793078 -3.596022e-03 -0.0037385146 -0.0039095455
## OwnOccMedVal          -0.0011169874 -1.094768e-03 -0.0010900314 -0.0011046790
## OwnOccHiQuart          0.0010568923  1.188957e-03  0.0013065635  0.0014080313
## RentLowQ              -0.0062367562 -6.475299e-03 -0.0067559684 -0.0070835444
## RentMedian            -0.0007926581 -5.703748e-04 -0.0003559548 -0.0001508101
## RentHighQ              0.0003526498  6.163921e-04  0.0008799413  0.0011431360
## MedRent                0.0025702052  3.046808e-03  0.0035287643  0.0040161325
## MedRentPctHousInc      0.0214547881  2.180625e-02  0.0221481451  0.0224832377
## MedOwnCostPctInc       0.0078672226  7.785554e-03  0.0076578688  0.0074837006
## MedOwnCostPctIncNoMtg -0.0048947087 -5.773672e-03 -0.0067228865 -0.0077425553
## NumInShelters          0.0423218159  4.282821e-02  0.0433161021  0.0437937397
## NumStreet              0.0532464398  5.515190e-02  0.0571493208  0.0592489638
## PctForeignBorn         0.0072777243  7.313820e-03  0.0073576350  0.0074128666
## PctBornSameState      -0.0098065459 -1.007438e-02 -0.0103274997 -0.0105643795
## PctSameHouse85        -0.0008584622 -5.387650e-04 -0.0002051257  0.0001409451
## PctSameCity85          0.0117045299  1.220978e-02  0.0127229284  0.0132431925
## PctSameState85        -0.0010111878 -1.006139e-03 -0.0009970886 -0.0009839827
## LandArea               0.0209053836  2.097514e-02  0.0210133986  0.0210240895
## PopDens                0.0113129173  1.122286e-02  0.0111200852  0.0110073957
## PctUsePubTrans         0.0098645096  9.809855e-03  0.0097172108  0.0095861176
##                                                                              
## (Intercept)            3.104225e-01  0.3138011869  3.174169e-01  3.212798e-01
## (Intercept)            .             .             .             .           
## state                 -4.549731e-04 -0.0004733052 -4.917507e-04 -5.102617e-04
## fold                  -5.459456e-04 -0.0005840200 -6.235874e-04 -6.645049e-04
## population             2.330559e-02  0.0225387113  2.169469e-02  2.077532e-02
## householdsize         -6.938832e-04 -0.0002764897  1.726834e-04  6.544534e-04
## racepctblack           6.496869e-02  0.0673031379  6.964296e-02  7.198406e-02
## racePctWhite          -5.739911e-02 -0.0590959671 -6.077235e-02 -6.242334e-02
## racePctAsian          -1.197508e-03 -0.0016807741 -2.200014e-03 -2.755080e-03
## racePctHisp            4.538583e-03  0.0043695897  4.208375e-03  4.057235e-03
## agePct12t21           -1.217384e-02 -0.0124188594 -1.262944e-02 -1.280580e-02
## agePct12t29           -1.613895e-02 -0.0171973603 -1.827677e-02 -1.938118e-02
## agePct16t24           -1.253317e-02 -0.0129495272 -1.333495e-02 -1.368922e-02
## agePct65up             5.065745e-03  0.0052890810  5.538798e-03  5.815782e-03
## numbUrban              2.697470e-02  0.0264601729  2.587180e-02  2.521067e-02
## pctUrban               1.514140e-02  0.0159003486  1.667422e-02  1.746139e-02
## medIncome             -4.441227e-03 -0.0040246030 -3.597466e-03 -3.159260e-03
## pctWWage              -1.404438e-02 -0.0141756556 -1.431400e-02 -1.446134e-02
## pctWFarmSelf          -1.377730e-02 -0.0136363611 -1.343288e-02 -1.316481e-02
## pctWInvInc            -3.702026e-02 -0.0377738634 -3.852809e-02 -3.928529e-02
## pctWSocSec             5.187817e-03  0.0053666194  5.567998e-03  5.793236e-03
## pctWPubAsst            2.348132e-02  0.0233913738  2.325014e-02  2.305797e-02
## pctWRetire            -1.164884e-02 -0.0123613854 -1.312623e-02 -1.394638e-02
## medFamInc             -7.829345e-03 -0.0075093876 -7.175797e-03 -6.829472e-03
## perCapInc             -1.036603e-03 -0.0007007382 -3.782543e-04 -7.348911e-05
## whitePerCap            1.457385e-02  0.0152584174  1.590018e-02  1.649098e-02
## blackPerCap           -1.071702e-02 -0.0110626430 -1.143688e-02 -1.183975e-02
## indianPerCap          -4.329751e-03 -0.0047973749 -5.306699e-03 -5.856610e-03
## AsianPerCap            1.034079e-02  0.0110440713  1.174247e-02  1.243273e-02
## OtherPerCap            1.329894e-02  0.0142286733  1.517116e-02  1.612371e-02
## HispPerCap             8.671137e-03  0.0093648569  1.004224e-02  1.069947e-02
## NumUnderPov            3.062387e-02  0.0300565958  2.941180e-02  2.869187e-02
## PctPopUnderPov         1.366603e-02  0.0131918691  1.264944e-02  1.203515e-02
## PctLess9thGrade        6.105437e-03  0.0055675671  4.992612e-03  4.379589e-03
## PctNotHSGrad           1.835626e-02  0.0183990035  1.843296e-02  1.845903e-02
## PctBSorMore           -6.508496e-03 -0.0063900204 -6.278618e-03 -6.175886e-03
## PctUnemployed          1.356554e-02  0.0128662147  1.209459e-02  1.124987e-02
## PctEmploy             -9.131680e-03 -0.0087218508 -8.266877e-03 -7.762716e-03
## PctEmplManu           -1.196022e-02 -0.0124447708 -1.292684e-02 -1.340532e-02
## PctEmplProfServ       -5.240431e-03 -0.0053776007 -5.519599e-03 -5.665970e-03
## PctOccupManu           3.731419e-03  0.0035888178  3.471734e-03  3.383502e-03
## PctOccupMgmtProf      -5.431544e-03 -0.0052463477 -5.077712e-03 -4.926755e-03
## MalePctDivorce         4.209377e-02  0.0428146670  4.351457e-02  4.419567e-02
## MalePctNevMarr         1.768069e-02  0.0181405343  1.863630e-02  1.917050e-02
## FemalePctDiv           3.868575e-02  0.0386247917  3.845373e-02  3.816815e-02
## TotalPctDiv            3.954178e-02  0.0397900327  3.996201e-02  4.005609e-02
## PersPerFam             1.342966e-02  0.0138185849  1.420569e-02  1.459100e-02
## PctFam2Par            -5.183753e-02 -0.0529065273 -5.394551e-02 -5.495311e-02
## PctKids2Par           -5.722882e-02 -0.0586408027 -6.003793e-02 -6.141979e-02
## PctYoungKids2Par      -4.447251e-02 -0.0453744413 -4.624762e-02 -4.709148e-02
## PctTeen2Par           -5.212043e-02 -0.0529458574 -5.369668e-02 -5.436907e-02
## PctWorkMomYoungKids    4.725722e-04  0.0004207655  3.413582e-04  2.325029e-04
## PctWorkMom            -1.421448e-02 -0.0149448715 -1.573716e-02 -1.659606e-02
## NumIlleg               5.261151e-02  0.0525691328  5.241085e-02  5.213523e-02
## PctIlleg               6.475170e-02  0.0668731968  6.901198e-02  7.116784e-02
## NumImmig               6.854703e-03  0.0049419637  2.933761e-03  8.350717e-04
## PctImmigRecent         7.611541e-04  0.0006213539  4.940029e-04  3.810570e-04
## PctImmigRec5           8.024904e-04  0.0004228513  2.971059e-05 -3.764974e-04
## PctImmigRec8           4.976688e-03  0.0046773748  4.357721e-03  4.017920e-03
## PctImmigRec10          1.027315e-02  0.0101885398  1.009269e-02  9.986566e-03
## PctRecentImmig         4.160961e-03  0.0040197333  3.886003e-03  3.761257e-03
## PctRecImmig5           6.073247e-03  0.0060052579  5.946816e-03  5.899814e-03
## PctRecImmig8           8.227838e-03  0.0082858774  8.360247e-03  8.453463e-03
## PctRecImmig10          9.690536e-03  0.0098146466  9.957622e-03  1.012186e-02
## PctSpeakEnglOnly      -1.400582e-03 -0.0010566999 -7.160754e-04 -3.811189e-04
## PctNotSpeakEnglWell    2.352340e-03  0.0019055950  1.450491e-03  9.880287e-04
## PctLargHouseFam        2.160035e-02  0.0217908062  2.194501e-02  2.206004e-02
## PctLargHouseOccup      1.614610e-02  0.0162376990  1.629310e-02  1.630845e-02
## PersPerOccupHous       4.589657e-03  0.0051087368  5.650082e-03  6.212761e-03
## PersPerOwnOccHous     -1.056071e-02 -0.0107532133 -1.095406e-02 -1.116714e-02
## PersPerRentOccHous     1.535416e-02  0.0155220960  1.565693e-02  1.575569e-02
## PctPersOwnOccup       -1.901691e-02 -0.0189376169 -1.886173e-02 -1.879493e-02
## PctPersDenseHous       2.348403e-02  0.0239786631  2.448975e-02  2.501927e-02
## PctHousLess3BR         1.841498e-02  0.0183782978  1.836296e-02  1.837410e-02
## MedNumBR              -5.003351e-03 -0.0047129796 -4.424625e-03 -4.140462e-03
## HousVacant             4.267910e-02  0.0436083495  4.456346e-02  4.555158e-02
## PctHousOccup          -3.181835e-02 -0.0329255452 -3.405625e-02 -3.521088e-02
## PctHousOwnOcc         -1.302766e-02 -0.0126172776 -1.220256e-02 -1.178780e-02
## PctVacantBoarded       3.832457e-02  0.0392242666  4.010577e-02  4.096870e-02
## PctVacMore6Mos        -5.417647e-03 -0.0059565865 -6.532447e-03 -7.145951e-03
## MedYrHousBuilt         4.376074e-03  0.0048038405  5.214114e-03  5.601622e-03
## PctHousNoPhone         1.957615e-02  0.0197069572  1.981704e-02  1.990686e-02
## PctWOFullPlumb         8.732764e-03  0.0082846145  7.809568e-03  7.309886e-03
## OwnOccLowQuart        -4.111421e-03 -0.0043460887 -4.615174e-03 -4.920024e-03
## OwnOccMedVal          -1.140055e-03 -0.0011971072 -1.276433e-03 -1.378342e-03
## OwnOccHiQuart          1.492265e-03  0.0015585359  1.606423e-03  1.635743e-03
## RentLowQ              -7.462496e-03 -0.0078971950 -8.391935e-03 -8.951008e-03
## RentMedian             4.436181e-05  0.0002293630  4.044981e-04  5.705207e-04
## RentHighQ              1.406482e-03  0.0016709569  1.938001e-03  2.209457e-03
## MedRent                4.509779e-03  0.0050111827  5.522405e-03  6.046006e-03
## MedRentPctHousInc      2.281456e-02  0.0231453386  2.347879e-02  2.381813e-02
## MedOwnCostPctInc       7.262908e-03  0.0069955940  6.682109e-03  6.323068e-03
## MedOwnCostPctIncNoMtg -8.832476e-03 -0.0099919667 -1.121982e-02 -1.251426e-02
## NumInShelters          4.427003e-02  0.0447540370  4.525443e-02  4.577931e-02
## NumStreet              6.146056e-02  0.0637928250  6.625301e-02  6.884672e-02
## PctForeignBorn         7.482674e-03  0.0075695864  7.675661e-03  7.802665e-03
## PctBornSameState      -1.078358e-02 -0.0109836278 -1.116299e-02 -1.132001e-02
## PctSameHouse85         4.978471e-04  0.0008641184  1.238645e-03  1.620716e-03
## PctSameCity85          1.376970e-02  0.0143017205  1.483881e-02  1.538079e-02
## PctSameState85        -9.666655e-04 -0.0009446818 -9.172627e-04 -8.834154e-04
## LandArea               2.101164e-02  0.0209807716  2.093635e-02  2.088335e-02
## PopDens                1.088709e-02  0.0107608647  1.062981e-02  1.049454e-02
## PctUsePubTrans         9.416206e-03  0.0092070667  8.958225e-03  8.669155e-03
##                                                                              
## (Intercept)            3.253981e-01  3.297768e-01  0.3344171300  0.3393168717
## (Intercept)            .             .             .             .           
## state                 -5.287883e-04 -5.472795e-04 -0.0005656833 -0.0005839472
## fold                  -7.066070e-04 -7.497081e-04 -0.0007936059 -0.0008380851
## population             1.978287e-02  1.871996e-02  0.0175894869  0.0163945155
## householdsize          1.169978e-03  1.720520e-03  0.0023072756  0.0029312801
## racepctblack           7.432259e-02  7.665504e-02  0.0789782719  0.0812895607
## racePctWhite          -6.404430e-02 -6.563081e-02 -0.0671786658 -0.0686838316
## racePctAsian          -3.345404e-03 -3.969969e-03 -0.0046272801 -0.0053153459
## racePctHisp            3.918749e-03  3.795609e-03  0.0036905137  0.0036061150
## agePct12t21           -1.294837e-02 -1.305775e-02 -0.0131347399 -0.0131802216
## agePct12t29           -2.051494e-02 -2.168276e-02 -0.0228897486 -0.0241413554
## agePct16t24           -1.401213e-02 -1.430345e-02 -0.0145629564 -0.0147904827
## agePct65up             6.120623e-03  6.453711e-03  0.0068152931  0.0072055145
## numbUrban              2.447816e-02  2.367583e-02  0.0228053131  0.0218682587
## pctUrban               1.826000e-02  1.906802e-02  0.0198832100  0.0207032293
## medIncome             -2.709559e-03 -2.247983e-03 -0.0017741369 -0.0012875597
## pctWWage              -1.461994e-02 -1.479235e-02 -0.0149814773 -0.0151905331
## pctWFarmSelf          -1.283046e-02 -1.242855e-02 -0.0119582766 -0.0114193914
## pctWInvInc            -4.004849e-02 -4.082123e-02 -0.0416073908 -0.0424112173
## pctWSocSec             6.043556e-03  6.320126e-03  0.0066240580  0.0069564265
## pctWPubAsst            2.281571e-02  2.252459e-02  0.0221861701  0.0218023176
## pctWRetire            -1.482471e-02 -1.576393e-02 -0.0167663591 -0.0178338864
## medFamInc             -6.471328e-03 -6.102173e-03 -0.0057226306 -0.0053330960
## perCapInc              2.092251e-04  4.657242e-04  0.0006920942  0.0008847073
## whitePerCap            1.702284e-02  1.748806e-02  0.0178792518  0.0181893430
## blackPerCap           -1.227048e-02 -1.272751e-02 -0.0132085362 -0.0137105790
## indianPerCap          -6.445293e-03 -7.070248e-03 -0.0077283478 -0.0084159196
## AsianPerCap            1.311183e-02  1.377703e-02  0.0144258785  0.0150562349
## OtherPerCap            1.708379e-02  1.804893e-02  0.0190167910  0.0199851231
## HispPerCap             1.133341e-02  1.194161e-02  0.0125223107  0.0130743864
## NumUnderPov            2.789942e-02  2.703722e-02  0.0261082432  0.0251158143
## PctPopUnderPov         1.134490e-02  1.057418e-02  0.0097182039  0.0087719868
## PctLess9thGrade        3.726942e-03  3.032582e-03  0.0022940019  0.0015084075
## PctNotHSGrad           1.847760e-02  1.848870e-02  0.0184921547  0.0184877865
## PctBSorMore           -6.082676e-03 -5.999242e-03 -0.0059253751 -0.0058604876
## PctUnemployed          1.033143e-02  9.339117e-03  0.0082734207  0.0071356171
## PctEmploy             -7.205112e-03 -6.589839e-03 -0.0059127918 -0.0051699787
## PctEmplManu           -1.387944e-02 -1.434873e-02 -0.0148130066 -0.0152723626
## PctEmplProfServ       -5.815756e-03 -5.967509e-03 -0.0061193459 -0.0062689887
## PctOccupManu           3.327309e-03  3.306342e-03  0.0033238176  0.0033829434
## PctOccupMgmtProf      -4.794193e-03 -4.680411e-03 -0.0045854133 -0.0045087215
## MalePctDivorce         4.486094e-02  4.551428e-02  0.0461605449  0.0468054543
## MalePctNevMarr         1.974570e-02  2.036452e-02  0.0210296142  0.0217435415
## FemalePctDiv           3.776438e-02  3.723954e-02  0.0365915247  0.0358188273
## TotalPctDiv            4.007168e-02  4.000917e-02  0.0398697753  0.0396554195
## PersPerFam             1.497445e-02  1.535591e-02  0.0157352340  0.0161124227
## PctFam2Par            -5.592878e-02 -5.687271e-02 -0.0577855582 -0.0586683247
## PctKids2Par           -6.278675e-02 -6.413976e-02 -0.0654801323 -0.0668094551
## PctYoungKids2Par      -4.790595e-02 -4.869122e-02 -0.0494476167 -0.0501755098
## PctTeen2Par           -5.495960e-02 -5.546508e-02 -0.0558824587 -0.0562089118
## PctWorkMomYoungKids    9.269099e-05 -7.914507e-05 -0.0002835288 -0.0005203421
## PctWorkMom            -1.752621e-02 -1.853212e-02 -0.0196180083 -0.0207877658
## NumIlleg               5.174095e-02  5.122660e-02  0.0505907348  0.0498320205
## PctIlleg               7.334063e-02  7.553013e-02  0.0777360894  0.0799582037
## NumImmig              -1.348882e-03 -3.612684e-03 -0.0059506555 -0.0083568027
## PctImmigRecent         2.844843e-04  2.062392e-04  0.0001482602  0.0001124707
## PctImmigRec5          -7.953399e-04 -1.226401e-03 -0.0016692714 -0.0021235355
## PctImmigRec8           3.658229e-03  3.278973e-03  0.0028805594  0.0024634907
## PctImmigRec10          9.871166e-03  9.747493e-03  0.0096165499  0.0094793277
## PctRecentImmig         3.646829e-03  3.543828e-03  0.0034530530  0.0033748966
## PctRecImmig5           5.865911e-03  5.846424e-03  0.0058422394  0.0058537377
## PctRecImmig8           8.567749e-03  8.704961e-03  0.0088665444  0.0090535271
## PctRecImmig10          1.030936e-02  1.052167e-02  0.0107599656  0.0110250242
## PctSpeakEnglOnly      -5.397936e-05  2.634126e-04  0.0005692841  0.0008619567
## PctNotSpeakEnglWell    5.187705e-04  4.290006e-05 -0.0004397203 -0.0009294984
## PctLargHouseFam        2.213254e-02  2.215884e-02  0.0221350979  0.0220574359
## PctLargHouseOccup      1.627956e-02  1.620209e-02  0.0160717479  0.0158844104
## PersPerOccupHous       6.796018e-03  7.399569e-03  0.0080237889  0.0086698268
## PersPerOwnOccHous     -1.139623e-02 -1.164483e-02 -0.0119160370 -0.0122125439
## PersPerRentOccHous     1.581590e-02  1.583568e-02  0.0158137608  0.0157494628
## PctPersOwnOccup       -1.874279e-02 -1.871081e-02 -0.0187042870 -0.0187283918
## PctPersDenseHous       2.556948e-02  2.614299e-02  0.0267427061  0.0273718210
## PctHousLess3BR         1.841624e-02  1.849316e-02  0.0186078582  0.0187625599
## MedNumBR              -3.862221e-03 -3.591169e-03 -0.0033281237 -0.0030734824
## HousVacant             4.657974e-02  4.765482e-02  0.0487835754  0.0499726111
## PctHousOccup          -3.638943e-02 -3.759136e-02 -0.0388155814 -0.0400603626
## PctHousOwnOcc         -1.137650e-02 -1.097143e-02 -0.0105746900 -0.0101878147
## PctVacantBoarded       4.181272e-02  4.263753e-02  0.0434428629  0.0442285575
## PctVacMore6Mos        -7.797686e-03 -8.488120e-03 -0.0092176153 -0.0099864447
## MedYrHousBuilt         5.961187e-03  6.287736e-03  0.0065763249  0.0068221782
## PctHousNoPhone         1.997691e-02  2.002779e-02  0.0200602518  0.0200752674
## PctWOFullPlumb         6.788032e-03  6.246714e-03  0.0056888845  0.0051177126
## OwnOccLowQuart        -5.261738e-03 -5.641226e-03 -0.0060592748 -0.0065166139
## OwnOccMedVal          -1.502896e-03 -1.649954e-03 -0.0018192152 -0.0020102422
## OwnOccHiQuart          1.646518e-03  1.638952e-03  0.0016134297  0.0015705205
## RentLowQ              -9.578757e-03 -1.027964e-02 -0.0110582387 -0.0119193062
## RentMedian             7.285925e-04  8.802502e-04  0.0010273800  0.0011721904
## RentHighQ              2.487515e-03  2.774674e-03  0.0030736893  0.0033875187
## MedRent                6.584958e-03  7.142587e-03  0.0077225058  0.0083285739
## MedRentPctHousInc      2.416659e-02  2.452740e-02  0.0249038593  0.0252992402
## MedOwnCostPctInc       5.919347e-03  5.472063e-03  0.0049825410  0.0044522863
## MedOwnCostPctIncNoMtg -1.387289e-02 -1.529272e-02 -0.0167701498 -0.0183010268
## NumInShelters          4.633617e-02  4.693200e-02  0.0475732935  0.0482660176
## NumStreet              7.157783e-02  7.444839e-02  0.0774585333  0.0806063369
## PctForeignBorn         7.952202e-03  8.125758e-03  0.0083247066  0.0085503192
## PctBornSameState      -1.145291e-02 -1.155977e-02 -0.0116386533 -0.0116876195
## PctSameHouse85         2.009955e-03  2.406200e-03  0.0028093684  0.0032193173
## PctSameCity85          1.592761e-02  1.647930e-02  0.0170357774  0.0175967866
## PctSameState85        -8.420792e-04 -7.922816e-04 -0.0007332639 -0.0006645634
## LandArea               2.082680e-02  2.077187e-02  0.0207236907  0.0206872692
## PopDens                1.035524e-02  1.021178e-02  0.0100637742  0.0099105870
## PctUsePubTrans         8.339323e-03  7.968234e-03  0.0075554717  0.0071007158
##                                                                              
## (Intercept)            0.3444702352  0.3498682167  0.3554988573  0.3613475139
## (Intercept)            .             .             .             .           
## state                 -0.0006020179 -0.0006198415 -0.0006373636 -0.0006545293
## fold                  -0.0008829220 -0.0009278882 -0.0009727557 -0.0010173001
## population             0.0151382741  0.0138241626  0.0124558099  0.0110371250
## householdsize          0.0035933836  0.0042942801  0.0050345544  0.0058147208
## racepctblack           0.0835865269  0.0858671334  0.0881296491  0.0903726174
## racePctWhite          -0.0701423745 -0.0715504611 -0.0729043429 -0.0742003581
## racePctAsian          -0.0060316806 -0.0067733208 -0.0075368576 -0.0083184786
## racePctHisp            0.0035450003  0.0035096940  0.0035026699  0.0035263656
## agePct12t21           -0.0131950863 -0.0131800947 -0.0131357551 -0.0130622266
## agePct12t29           -0.0254433865 -0.0268019900 -0.0282236703 -0.0297153143
## agePct16t24           -0.0149859476 -0.0151494042 -0.0152810829 -0.0153814165
## agePct65up             0.0076244564  0.0080721771  0.0085487589  0.0090543513
## numbUrban              0.0208664107  0.0198016834  0.0186762568  0.0174926490
## pctUrban               0.0215256207  0.0223478704  0.0231674445  0.0239818284
## medIncome             -0.0007876885 -0.0002738343  0.0002548220  0.0007992106
## pctWWage              -0.0154230788 -0.0156830417 -0.0159747387 -0.0163028881
## pctWFarmSelf          -0.0108121972 -0.0101375922 -0.0093970726 -0.0085927203
## pctWInvInc            -0.0432372200 -0.0440901613 -0.0449750202 -0.0458969636
## pctWSocSec             0.0073182805  0.0077106561  0.0081345687  0.0085909814
## pctWPubAsst            0.0213752050  0.0209073012  0.0204013598  0.0198603970
## pctWRetire            -0.0189678566 -0.0201690072 -0.0214374314 -0.0227725683
## medFamInc             -0.0049336904 -0.0045242263 -0.0041041827 -0.0036726929
## perCapInc              0.0010402318  0.0011556226  0.0012280988  0.0012551159
## whitePerCap            0.0184115157  0.0185391688  0.0185658624  0.0184852718
## blackPerCap           -0.0142300767 -0.0147629819 -0.0153048785 -0.0158511072
## indianPerCap          -0.0091288371 -0.0098626308 -0.0106126000 -0.0113739244
## AsianPerCap            0.0156662417  0.0162543334  0.0168192298  0.0173599314
## OtherPerCap            0.0209517294  0.0219144717  0.0228712560  0.0238200285
## HispPerCap             0.0135973324  0.0140912050  0.0145565834  0.0149945285
## NumUnderPov            0.0240637473  0.0229565060  0.0217993114  0.0205981901
## PctPopUnderPov         0.0077304621  0.0065885625  0.0053412957  0.0039838116
## PctLess9thGrade        0.0006728657 -0.0002155658 -0.0011597322 -0.0021623031
## PctNotHSGrad           0.0184754934  0.0184553216  0.0184274752  0.0183923021
## PctBSorMore           -0.0058036390 -0.0057535164 -0.0057083878 -0.0056660494
## PctUnemployed          0.0059277985  0.0046528774  0.0033145631  0.0019173331
## PctEmploy             -0.0043574577 -0.0034712583 -0.0025073151 -0.0014614308
## PctEmplManu           -0.0157271465 -0.0161779500 -0.0166255996 -0.0170711458
## PctEmplProfServ       -0.0064138112 -0.0065508801 -0.0066770110 -0.0067888443
## PctOccupManu           0.0034868397  0.0036384628  0.0038405513  0.0040956010
## PctOccupMgmtProf      -0.0044492712 -0.0044053435 -0.0043745427 -0.0043538105
## MalePctDivorce         0.0474554161  0.0481173783  0.0487986768  0.0495069188
## MalePctNevMarr         0.0225087441  0.0233274559  0.0242016680  0.0251331196
## FemalePctDiv           0.0349204413  0.0338957322  0.0327443672  0.0314662737
## TotalPctDiv            0.0393685997  0.0390122979  0.0385899163  0.0381052133
## PersPerFam             0.0164877088  0.0168616685  0.0172352758  0.0176099060
## PctFam2Par            -0.0595221944 -0.0603485025 -0.0611487181 -0.0619244444
## PctKids2Par           -0.0681295380 -0.0694424276 -0.0707504245 -0.0720560898
## PctYoungKids2Par      -0.0508753097 -0.0515474646 -0.0521924473 -0.0528107226
## PctTeen2Par           -0.0564418767 -0.0565791272 -0.0566188163 -0.0565595095
## PctWorkMomYoungKids   -0.0007887359 -0.0010870549 -0.0014127782 -0.0017624814
## PctWorkMom            -0.0220448912 -0.0233924368 -0.0248329746 -0.0263685794
## NumIlleg               0.0489493505  0.0479419110  0.0468091500  0.0455506933
## PctIlleg               0.0821960941  0.0844492574  0.0867169920  0.0889983268
## NumImmig              -0.0108248380 -0.0133482725 -0.0159205363 -0.0185350806
## PctImmigRecent         0.0001007656  0.0001149773  0.0001568356  0.0002279318
## PctImmigRec5          -0.0025887787 -0.0030645981 -0.0035506073 -0.0040464257
## PctImmigRec8           0.0020283569  0.0015758323  0.0011066847  0.0006217957
## PctImmigRec10          0.0093367835  0.0091898330  0.0090393562  0.0088862114
## PctRecentImmig         0.0033092552  0.0032554612  0.0032122552  0.0031777904
## PctRecImmig5           0.0058807551  0.0059225919  0.0059780558  0.0060455238
## PctRecImmig8           0.0092665610  0.0095059855  0.0097718999  0.0100642256
## PctRecImmig10          0.0113173443  0.0116371911  0.0119846407  0.0123595987
## PctSpeakEnglOnly       0.0011397963  0.0014011868  0.0016445269  0.0018682391
## PctNotSpeakEnglWell   -0.0014270817 -0.0019333457 -0.0024493938 -0.0029765544
## PctLargHouseFam        0.0219219978  0.0217250246  0.0214628968  0.0211321747
## PctLargHouseOccup      0.0156362021  0.0153235592  0.0149432695  0.0144925011
## PersPerOccupHous       0.0093396609  0.0100361114  0.0107628161  0.0115241755
## PersPerOwnOccHous     -0.0125366768 -0.0128904786 -0.0132757969 -0.0136943723
## PersPerRentOccHous     0.0156426247  0.0154935302  0.0153028347  0.0150714957
## PctPersOwnOccup       -0.0187881044 -0.0188882472 -0.0190335124 -0.0192285037
## PctPersDenseHous       0.0280337304  0.0287320123  0.0294704016  0.0302527868
## PctHousLess3BR         0.0189587602  0.0191972981  0.0194784527  0.0198020419
## MedNumBR              -0.0028272652 -0.0025891648 -0.0023585976 -0.0021347504
## HousVacant             0.0512283132  0.0525567654  0.0539636418  0.0554541122
## PctHousOccup          -0.0413233577 -0.0426015832 -0.0438914403 -0.0451887448
## PctHousOwnOcc         -0.0098118623 -0.0094475282 -0.0090952329 -0.0087551848
## PctVacantBoarded       0.0449944843  0.0457405525  0.0464666830  0.0471727904
## PctVacMore6Mos        -0.0107947998 -0.0116427887 -0.0125304209 -0.0134575797
## MedYrHousBuilt         0.0070207517  0.0071678251  0.0072596093  0.0072928583
## PctHousNoPhone         0.0200740739  0.0200582144  0.0200295602  0.0199903082
## PctWOFullPlumb         0.0045365375  0.0039488084  0.0033580140  0.0027676074
## OwnOccLowQuart        -0.0070139867 -0.0075522027 -0.0081321680 -0.0087548874
## OwnOccMedVal          -0.0022224686 -0.0024551890 -0.0027075305 -0.0029784194
## OwnOccHiQuart          0.0015109958  0.0014358484  0.0013463051  0.0012438290
## RentLowQ              -0.0128677264 -0.0139085250 -0.0150468625 -0.0162880370
## RentMedian             0.0013171775  0.0014650767  0.0016188066  0.0017814117
## RentHighQ              0.0037192634  0.0040721066  0.0044492577  0.0048539055
## MedRent                0.0089648535  0.0096355899  0.0103452029  0.0110982882
## MedRentPctHousInc      0.0257167695  0.0261595543  0.0266305117  0.0271322925
## MedOwnCostPctInc       0.0038829583  0.0032763488  0.0026343573  0.0019589601
## MedOwnCostPctIncNoMtg -0.0198807253 -0.0215042089 -0.0231661108 -0.0248608048
## NumInShelters          0.0490154758  0.0498262048  0.0507018629  0.0516451617
## NumStreet              0.0838877428  0.0872965087  0.0908242248  0.0944604006
## PctForeignBorn         0.0088037644  0.0090861095  0.0093983152  0.0097412262
## PctBornSameState      -0.0117048791 -0.0116888697 -0.0116383383 -0.0115523958
## PctSameHouse85         0.0036357342  0.0040580579  0.0044854530  0.0049168317
## PctSameCity85          0.0181618325  0.0187301451  0.0193006845  0.0198721677
## PctSameState85        -0.0005860513 -0.0004979288 -0.0004006960 -0.0002951055
## LandArea               0.0206673098  0.0206680493  0.0206931208  0.0207454530
## PopDens                0.0097514057  0.0095852528  0.0094110116  0.0092274502
## PctUsePubTrans         0.0066037700  0.0060645821  0.0054832744  0.0048601769
##                                                                              
## (Intercept)            0.3673971439  3.736286e-01  3.800211e-01  0.3865522348
## (Intercept)            .             .             .             .           
## state                 -0.0006712837 -6.875724e-04 -7.033420e-04 -0.0007185417
## fold                  -0.0010613053 -1.104567e-03 -1.146895e-03 -0.0011881166
## population             0.0095723255  8.065939e-03  6.522779e-03  0.0049479075
## householdsize          0.0066352372  7.496486e-03  8.398724e-03  0.0093420113
## racepctblack           0.0925948276  9.479529e-02  9.697323e-02  0.0991280784
## racePctWhite          -0.0754349453 -7.660466e-02 -7.770622e-02 -0.0787364635
## racePctAsian          -0.0091140162 -9.919003e-03 -1.072873e-02 -0.0115383236
## racePctHisp            0.0035831912  3.675525e-03  3.805698e-03  0.0039759554
## agePct12t21           -0.0129592589 -1.282617e-02 -1.266185e-02 -0.0124647837
## agePct12t29           -0.0312842157 -3.293809e-02 -3.468504e-02 -0.0365335540
## agePct16t24           -0.0154510394 -1.549076e-02 -1.550151e-02 -0.0154842740
## agePct65up             0.0095892025  1.015367e-02  1.074821e-02  0.0113733613
## numbUrban              0.0162537547  1.496285e-02  1.362358e-02  0.0122398919
## pctUrban               0.0247885631  2.558528e-02  2.636972e-02  0.0271397868
## medIncome              0.0013603337  1.939214e-03  2.536841e-03  0.0031541210
## pctWWage              -0.0166725991 -1.708934e-02 -1.755889e-02 -0.0180872840
## pctWFarmSelf          -0.0077271736 -6.803583e-03 -5.825556e-03 -0.0047970942
## pctWInvInc            -0.0468613176 -4.787353e-02 -4.893915e-02 -0.0500637207
## pctWSocSec             0.0090807567  9.604601e-03  1.016302e-02  0.0107562626
## pctWPubAsst            0.0192876604  1.868659e-02  1.806076e-02  0.0174138333
## pctWRetire            -0.0241732103 -2.563752e-02 -2.716305e-02 -0.0287467450
## medFamInc             -0.0032285455 -2.770196e-03 -2.295786e-03 -0.0018031661
## perCapInc              0.0012343413  1.163636e-03  1.041045e-03  0.0008647982
## whitePerCap            0.0182911529  1.797732e-02  1.753765e-02  0.0169660592
## blackPerCap           -0.0163968980 -1.693751e-02 -1.746835e-02 -0.0179851309
## indianPerCap          -0.0121417714 -1.291140e-02 -1.367823e-02 -0.0144379502
## AsianPerCap            0.0178757134  1.836612e-02  1.883094e-02  0.0192702306
## OtherPerCap            0.0247587767  2.568553e-02  2.659839e-02  0.0274955058
## HispPerCap             0.0154065389  1.579450e-02  1.616065e-02  0.0165075034
## NumUnderPov            0.0193599706  1.809224e-02  1.680331e-02  0.0155021387
## PctPopUnderPov         0.0025114613  9.198499e-04 -7.951150e-04 -0.0026371808
## PctLess9thGrade       -0.0032257173 -4.352147e-03 -5.543469e-03 -0.0068012431
## PctNotHSGrad           0.0183502696  1.830195e-02  1.824800e-02  0.0181891845
## PctBSorMore           -0.0056237826 -5.578327e-03 -5.525871e-03 -0.0054620488
## PctUnemployed          0.0004664097 -1.032261e-03 -2.572029e-03 -0.0041455632
## PctEmploy             -0.0003292691  8.936276e-04  2.211808e-03  0.0036298800
## PctEmplManu           -0.0175158459 -1.796114e-02 -1.840861e-02 -0.0188599789
## PctEmplProfServ       -0.0068829417 -6.955900e-03 -7.004473e-03 -0.0070257055
## PctOccupManu           0.0044058621  4.773349e-03  5.199850e-03  0.0056869411
## PctOccupMgmtProf      -0.0043394593 -4.327212e-03 -4.312243e-03 -0.0042892178
## MalePctDivorce         0.0502498968  5.103552e-02  5.187178e-02  0.0527666407
## MalePctNevMarr         0.0261233212  2.717360e-02  2.828517e-02  0.0294591858
## FemalePctDiv           0.0300616109  2.853074e-02  2.687418e-02  0.0250925630
## TotalPctDiv            0.0375622274  3.696519e-02  3.631841e-02  0.0356262051
## PersPerFam             0.0179872856  1.836940e-02  1.875838e-02  0.0191563632
## PctFam2Par            -0.0626774152 -6.340948e-02 -6.412260e-02 -0.0648188080
## PctKids2Par           -0.0733622359 -7.467190e-02 -7.598834e-02 -0.0773149578
## PctYoungKids2Par      -0.0534026999 -5.396868e-02 -5.450880e-02 -0.0550229829
## PctTeen2Par           -0.0564002161 -5.614043e-02 -5.578015e-02 -0.0553199488
## PctWorkMomYoungKids   -0.0021318218 -2.515554e-03 -2.907573e-03 -0.0033009953
## PctWorkMom            -0.0280008312 -2.973084e-02 -3.155928e-02 -0.0334864500
## NumIlleg               0.0441662462  4.265552e-02  4.101816e-02  0.0392537981
## PctIlleg               0.0912919612  9.359622e-02  9.590901e-02  0.0982278053
## NumImmig              -0.0211854353 -2.386521e-02 -2.656809e-02 -0.0292877115
## PctImmigRecent         0.0003296965  4.633875e-04  6.300829e-04  0.0008306739
## PctImmigRec5          -0.0045516549 -5.065851e-03 -5.588503e-03 -0.0061190111
## PctImmigRec8           0.0001221860 -3.909654e-04 -9.163098e-04 -0.0014523264
## PctImmigRec10          0.0087312484  8.575316e-03  8.419265e-03  0.0082639473
## PctRecentImmig         0.0031496634  3.124961e-03  3.100316e-03  0.0030719753
## PctRecImmig5           0.0061230093  6.208224e-03  6.298632e-03  0.0063914938
## PctRecImmig8           0.0103827499  1.072715e-02  1.109703e-02  0.0114918940
## PctRecImmig10          0.0127617998  1.319080e-02  1.364597e-02  0.0141265064
## PctSpeakEnglOnly       0.0020707822  2.250659e-03  2.406420e-03  0.0025366612
## PctNotSpeakEnglWell   -0.0035163731 -4.070596e-03 -4.641151e-03 -0.0052301269
## PctLargHouseFam        0.0207296356  2.025231e-02  1.969751e-02  0.0190628832
## PctLargHouseOccup      0.0139688239  1.337023e-02  1.269513e-02  0.0119423946
## PersPerOccupHous       0.0123252792  1.317183e-02  1.407006e-02  0.0150266833
## PersPerOwnOccHous     -0.0141479135 -1.463815e-02 -1.516687e-02 -0.0157359458
## PersPerRentOccHous     0.0148007100  1.449185e-02  1.414641e-02  0.0137659254
## PctPersOwnOccup       -0.0194777749 -1.978586e-02 -2.015727e-02 -0.0205965369
## PctPersDenseHous       0.0310832169  3.196591e-02  3.290527e-02  0.0339058649
## PctHousLess3BR         0.0201675148  2.057403e-02  2.102055e-02  0.0215058679
## MedNumBR              -0.0019166212 -1.703057e-03 -1.492787e-03 -0.0012844547
## HousVacant             0.0570327681  5.870357e-02  6.046981e-02  0.0623340778
## PctHousOccup          -0.0464887674 -4.778628e-02 -4.907561e-02 -0.0503507174
## PctHousOwnOcc         -0.0084274102 -8.111758e-03 -7.807887e-03 -0.0075152360
## PctVacantBoarded       0.0478587702  4.852449e-02  4.916980e-02  0.0497945150
## PctVacMore6Mos        -0.0144239848 -1.542915e-02 -1.647234e-02 -0.0175525271
## MedYrHousBuilt         0.0072649742  7.174099e-03  7.019191e-03  0.0068000784
## PctHousNoPhone         0.0199429527  1.989024e-02  1.983509e-02  0.0197805645
## PctWOFullPlumb         0.0021809319  1.601153e-03  1.031203e-03  0.0004737308
## OwnOccLowQuart        -0.0094214436 -1.013297e-02 -1.089060e-02 -0.0116955069
## OwnOccMedVal          -0.0032665529 -3.570388e-03 -3.888150e-03 -0.0042178506
## OwnOccHiQuart          0.0011301047  1.007014e-03  8.766055e-04  0.0007410733
## RentLowQ              -0.0176374888 -1.910080e-02 -2.068366e-02 -0.0223918653
## RentMedian             0.0019560123  2.145767e-03  2.353848e-03  0.0025834284
## RentHighQ              0.0052891823  5.758134e-03  6.263691e-03  0.0068086321
## MedRent                0.0118996203  1.275415e-02  1.366698e-02  0.0146433533
## MedRentPctHousInc      0.0276672041  2.823714e-02  2.884349e-02  0.0294871287
## MedOwnCostPctInc       0.0012521726  5.160078e-04 -2.475656e-04 -0.0010366599
## MedOwnCostPctIncNoMtg -0.0265824683 -2.832514e-02 -3.008277e-02 -0.0318492708
## NumInShelters          0.0526578405  5.374068e-02  5.489351e-02  0.0561153067
## NumStreet              0.0981926128  1.020067e-01  1.058870e-01  0.1098167250
## PctForeignBorn         0.0101155588  1.052189e-02  1.096065e-02  0.0114321007
## PctBornSameState      -0.0114305465 -1.127270e-02 -1.107915e-02 -0.0108505773
## PctSameHouse85         0.0053509097  5.786275e-03  6.221457e-03  0.0066549848
## PctSameCity85          0.0204431091  2.101187e-02  2.157668e-02  0.0221357355
## PctSameState85        -0.0001821139 -6.283448e-05  6.150684e-05  0.0001896145
## LandArea               0.0208272021  2.093971e-02  2.108347e-02  0.0212581359
## PopDens                0.0090332536  8.827063e-03  8.607525e-03  0.0083733380
## PctUsePubTrans         0.0041958604  3.491163e-03  2.747206e-03  0.0019654048
##                                                                              
## (Intercept)            3.931989e-01  0.4001914928  0.4065853445  0.4137263857
## (Intercept)            .             .             .             .           
## state                 -7.331233e-04 -0.0007474824 -0.0007606828 -0.0007728637
## fold                  -1.228080e-03 -0.0012669860 -0.0013041319 -0.0013394548
## population             3.346585e-03  0.0018513000  0.0001875190 -0.0014148632
## householdsize          1.032612e-02  0.0114168468  0.0123968432  0.0135609688
## racepctblack           1.012595e-01  0.1033259308  0.1055354584  0.1075275963
## racePctWhite          -7.969244e-02 -0.0805442989 -0.0814398127 -0.0821416120
## racePctAsian          -1.234280e-02 -0.0131417935 -0.0139088381 -0.0147200135
## racePctHisp            4.188422e-03  0.0044385343  0.0047791033  0.0051416622
## agePct12t21           -1.223310e-02 -0.0118280198 -0.0116375667 -0.0111243042
## agePct12t29           -3.849241e-02 -0.0406111689 -0.0427073449 -0.0451715500
## agePct16t24           -1.544004e-02 -0.0154378022 -0.0152580730 -0.0152178709
## agePct65up             1.202967e-02  0.0128020956  0.0134334271  0.0143171371
## numbUrban              1.081603e-02  0.0092725129  0.0078918699  0.0063018138
## pctUrban               2.789354e-02  0.0286201450  0.0293410983  0.0300301097
## medIncome              3.791831e-03  0.0044516526  0.0051138723  0.0057678503
## pctWWage              -1.868075e-02 -0.0194758664 -0.0201410848 -0.0211167193
## pctWFarmSelf          -3.722526e-03 -0.0025961771 -0.0014905194 -0.0002652580
## pctWInvInc            -5.125278e-02 -0.0525903540 -0.0539467822 -0.0554479878
## pctWSocSec             1.138433e-02  0.0121784564  0.0128064427  0.0136500872
## pctWPubAsst            1.674952e-02  0.0161399824  0.0154805234  0.0148660141
## pctWRetire            -3.038500e-02 -0.0320233973 -0.0337741637 -0.0355614200
## medFamInc             -1.289921e-03 -0.0006936427 -0.0001949198  0.0004041688
## perCapInc              6.333114e-04  0.0003476780  0.0000209513 -0.0004401095
## whitePerCap            1.625655e-02  0.0153195038  0.0144005649  0.0131254131
## blackPerCap           -1.848396e-02 -0.0189614705 -0.0194174757 -0.0198622562
## indianPerCap          -1.518657e-02 -0.0159218453 -0.0166337569 -0.0173375296
## AsianPerCap            1.968426e-02  0.0200855672  0.0204395501  0.0207830675
## OtherPerCap            2.837512e-02  0.0292302497  0.0300749019  0.0308796036
## HispPerCap             1.683779e-02  0.0171551428  0.0174528444  0.0177431228
## NumUnderPov            1.419828e-02  0.0127956131  0.0116045838  0.0103244202
## PctPopUnderPov        -4.609709e-03 -0.0068132878 -0.0089511902 -0.0114154301
## PctLess9thGrade       -8.126688e-03 -0.0096444051 -0.0109826439 -0.0126462353
## PctNotHSGrad           1.812640e-02  0.0178886153  0.0179767139  0.0177604714
## PctBSorMore           -5.381962e-03 -0.0052144979 -0.0051368454 -0.0049072384
## PctUnemployed         -5.744885e-03 -0.0075407008 -0.0090371076 -0.0107873540
## PctEmploy              5.152475e-03  0.0070782783  0.0086472401  0.0107626107
## PctEmplManu           -1.931702e-02 -0.0197320630 -0.0202190720 -0.0206815504
## PctEmplProfServ       -7.017054e-03 -0.0070462490 -0.0069752329 -0.0068971313
## PctOccupManu           6.235986e-03  0.0067318873  0.0074413259  0.0081470317
## PctOccupMgmtProf      -4.252343e-03 -0.0041068481 -0.0039934940 -0.0038658977
## MalePctDivorce         5.372806e-02  0.0547086026  0.0559222304  0.0571079368
## MalePctNevMarr         3.069683e-02  0.0318371695  0.0333157175  0.0346800067
## FemalePctDiv           2.318656e-02  0.0210610448  0.0189711284  0.0166553788
## TotalPctDiv            3.489278e-02  0.0339738461  0.0332540263  0.0323144482
## PersPerFam             1.956541e-02  0.0200820289  0.0203269673  0.0209565829
## PctFam2Par            -6.550020e-02 -0.0660072193 -0.0666317352 -0.0673060403
## PctKids2Par           -7.865531e-02 -0.0800549143 -0.0812280042 -0.0827806401
## PctYoungKids2Par      -5.551091e-02 -0.0560502334 -0.0563284339 -0.0568103012
## PctTeen2Par           -5.476101e-02 -0.0541637064 -0.0533328811 -0.0524881194
## PctWorkMomYoungKids   -3.688256e-03 -0.0039821442 -0.0044213134 -0.0046434969
## PctWorkMom            -3.551232e-02 -0.0376960049 -0.0398289329 -0.0422376132
## NumIlleg               3.736200e-02  0.0352450393  0.0331322234  0.0306704727
## PctIlleg               1.005496e-01  0.1029667389  0.1051924035  0.1075396843
## NumImmig              -3.201770e-02 -0.0349049813 -0.0375607350 -0.0404411433
## PctImmigRecent         1.065853e-03  0.0013343298  0.0016326849  0.0019885409
## PctImmigRec5          -6.656681e-03 -0.0071468929 -0.0077519165 -0.0082490558
## PctImmigRec8          -1.997325e-03 -0.0025404086 -0.0031059762 -0.0036651660
## PctImmigRec10          8.110209e-03  0.0079371621  0.0077935397  0.0076284653
## PctRecentImmig         3.035857e-03  0.0029288012  0.0029003603  0.0027874986
## PctRecImmig5           6.483909e-03  0.0065607513  0.0066250061  0.0067193016
## PctRecImmig8           1.191120e-02  0.0123873636  0.0128019711  0.0133443052
## PctRecImmig10          1.463141e-02  0.0152305189  0.0157122016  0.0163496154
## PctSpeakEnglOnly       2.640025e-03  0.0026192059  0.0027763586  0.0027055786
## PctNotSpeakEnglWell   -5.839756e-03 -0.0064376473 -0.0071154922 -0.0078382224
## PctLargHouseFam        1.834640e-02  0.0175756973  0.0166937804  0.0156517128
## PctLargHouseOccup      1.111136e-02  0.0101236480  0.0092305226  0.0080216061
## PersPerOccupHous       1.604882e-02  0.0171422289  0.0183352358  0.0196700855
## PersPerOwnOccHous     -1.634732e-02 -0.0170214319 -0.0177232842 -0.0184189327
## PersPerRentOccHous     1.335191e-02  0.0129098527  0.0124443745  0.0118928942
## PctPersOwnOccup       -2.110817e-02 -0.0217455343 -0.0223688550 -0.0230920489
## PctPersDenseHous       3.497246e-02  0.0360994245  0.0373222121  0.0385885108
## PctHousLess3BR         2.202870e-02  0.0226437486  0.0232115027  0.0238384855
## MedNumBR              -1.076644e-03 -0.0008761914 -0.0006700246 -0.0004518270
## HousVacant             6.429823e-02  0.0663083383  0.0685124811  0.0707288039
## PctHousOccup          -5.160524e-02 -0.0528448416 -0.0540327673 -0.0552013718
## PctHousOwnOcc         -7.232999e-03 -0.0071131866 -0.0067383739 -0.0066029548
## PctVacantBoarded       5.039844e-02  0.0510438134  0.0515538131  0.0521477147
## PctVacMore6Mos        -1.866836e-02 -0.0198253932 -0.0210289011 -0.0222446697
## MedYrHousBuilt         6.517505e-03  0.0061644224  0.0057327093  0.0053019930
## PctHousNoPhone         1.972976e-02  0.0198163411  0.0196560367  0.0197289992
## PctWOFullPlumb        -6.892911e-05 -0.0005679224 -0.0010915917 -0.0015781215
## OwnOccLowQuart        -1.254883e-02 -0.0135125208 -0.0143351662 -0.0154056037
## OwnOccMedVal          -4.557325e-03 -0.0049719048 -0.0052406282 -0.0056183028
## OwnOccHiQuart          6.027376e-04  0.0004060414  0.0003065315  0.0001816864
## RentLowQ              -2.423119e-02 -0.0263560904 -0.0283588201 -0.0307219694
## RentMedian             2.837666e-03  0.0029764824  0.0033488230  0.0036440390
## RentHighQ              7.395540e-03  0.0079975030  0.0086171809  0.0093985860
## MedRent                1.568860e-02  0.0169085278  0.0179653525  0.0193933350
## MedRentPctHousInc      3.016831e-02  0.0309966579  0.0317193208  0.0325431748
## MedOwnCostPctInc      -1.849498e-03 -0.0026312957 -0.0035359016 -0.0043962653
## MedOwnCostPctIncNoMtg -3.361858e-02 -0.0353985765 -0.0371111784 -0.0388908394
## NumInShelters          5.740416e-02  0.0588246883  0.0602250908  0.0617773856
## NumStreet              1.137780e-01  0.1178408933  0.1217419140  0.1257814425
## PctForeignBorn         1.193637e-02  0.0125155936  0.0130577752  0.0137032560
## PctBornSameState      -1.058803e-02 -0.0103317820 -0.0099899470 -0.0096698127
## PctSameHouse85         7.085436e-03  0.0074862811  0.0078715586  0.0082904246
## PctSameCity85          2.268716e-02  0.0232684844  0.0237832348  0.0243178148
## PctSameState85         3.201653e-04  0.0005248734  0.0006172439  0.0007815993
## LandArea               2.146251e-02  0.0218015583  0.0219271762  0.0223512677
## PopDens                8.123313e-03  0.0079000735  0.0076081081  0.0073421685
## PctUsePubTrans         1.147462e-03  0.0003247672 -0.0005776836 -0.0014627725
##                                                                              
## (Intercept)            4.205756e-01  4.273401e-01  0.4343705841  4.409667e-01
## (Intercept)            .             .             .             .           
## state                 -7.848166e-04 -7.956455e-04 -0.0008059285 -8.153399e-04
## fold                  -1.373472e-03 -1.405596e-03 -0.0014361856 -1.465085e-03
## population            -2.990755e-03 -4.633397e-03 -0.0061250990 -7.658674e-03
## householdsize          1.471150e-02  1.586906e-02  0.0171609535  1.838362e-02
## racepctblack           1.095982e-01  1.116490e-01  0.1136566011  1.157150e-01
## racePctWhite          -8.280845e-02 -8.339320e-02 -0.0838725595 -8.432654e-02
## racePctAsian          -1.544339e-02 -1.615705e-02 -0.0168541251 -1.749084e-02
## racePctHisp            5.556894e-03  6.027065e-03  0.0065387279  7.140034e-03
## agePct12t21           -1.070054e-02 -1.023896e-02 -0.0095278833 -8.913422e-03
## agePct12t29           -4.762275e-02 -5.028178e-02 -0.0531976007 -5.612650e-02
## agePct16t24           -1.508251e-02 -1.491553e-02 -0.0148360709 -1.463237e-02
## agePct65up             1.507546e-02  1.589700e-02  0.0168596188  1.773469e-02
## numbUrban              4.772360e-03  3.228540e-03  0.0015799948  1.894579e-05
## pctUrban               3.070214e-02  3.135326e-02  0.0319764570  3.257660e-02
## medIncome              6.524361e-03  7.277400e-03  0.0080886785  8.896378e-03
## pctWWage              -2.207249e-02 -2.310781e-02 -0.0244119387 -2.571679e-02
## pctWFarmSelf           9.323841e-04  2.157680e-03  0.0034170479  4.657524e-03
## pctWInvInc            -5.701769e-02 -5.865632e-02 -0.0604661618 -6.237330e-02
## pctWSocSec             1.448827e-02  1.531110e-02  0.0163204441  1.728680e-02
## pctWPubAsst            1.423262e-02  1.359437e-02  0.0130277258  1.246820e-02
## pctWRetire            -3.734753e-02 -3.919021e-02 -0.0410262183 -4.287941e-02
## medFamInc              1.017056e-03  1.657379e-03  0.0024160580  3.149358e-03
## perCapInc             -9.106075e-04 -1.448452e-03 -0.0020490568 -2.678382e-03
## whitePerCap            1.175426e-02  1.023493e-02  0.0083927670  6.486433e-03
## blackPerCap           -2.028583e-02 -2.067235e-02 -0.0210534058 -2.139391e-02
## indianPerCap          -1.801377e-02 -1.866335e-02 -0.0192941081 -1.989058e-02
## AsianPerCap            2.109795e-02  2.139170e-02  0.0216674302  2.192252e-02
## OtherPerCap            3.166789e-02  3.243363e-02  0.0331628362  3.387578e-02
## HispPerCap             1.801620e-02  1.829337e-02  0.0185660642  1.883536e-02
## NumUnderPov            9.134677e-03  8.014313e-03  0.0069101982  5.942052e-03
## PctPopUnderPov        -1.390911e-02 -1.654089e-02 -0.0194043077 -2.231052e-02
## PctLess9thGrade       -1.424065e-02 -1.590304e-02 -0.0177713570 -1.956776e-02
## PctNotHSGrad           1.768374e-02  1.764210e-02  0.0174199665  1.737350e-02
## PctBSorMore           -4.687401e-03 -4.442954e-03 -0.0040569938 -3.671562e-03
## PctUnemployed         -1.244754e-02 -1.403060e-02 -0.0157530914 -1.730932e-02
## PctEmploy              1.286458e-02  1.503272e-02  0.0176463890  2.021234e-02
## PctEmplManu           -2.115538e-02 -2.166065e-02 -0.0221335516 -2.264678e-02
## PctEmplProfServ       -6.798155e-03 -6.649015e-03 -0.0065281800 -6.378473e-03
## PctOccupManu           8.890769e-03  9.752705e-03  0.0105959161  1.152682e-02
## PctOccupMgmtProf      -3.618415e-03 -3.365714e-03 -0.0029997892 -2.539021e-03
## MalePctDivorce         5.845963e-02  5.991583e-02  0.0614868552  6.323163e-02
## MalePctNevMarr         3.614418e-02  3.773905e-02  0.0392974638  4.101099e-02
## FemalePctDiv           1.422835e-02  1.171352e-02  0.0089933993  6.207460e-03
## TotalPctDiv            3.139680e-02  3.048523e-02  0.0293971088  2.838707e-02
## PersPerFam             2.137684e-02  2.184095e-02  0.0224444805  2.287927e-02
## PctFam2Par            -6.782199e-02 -6.841230e-02 -0.0689081911 -6.936375e-02
## PctKids2Par           -8.415746e-02 -8.556443e-02 -0.0871348739 -8.855777e-02
## PctYoungKids2Par      -5.717666e-02 -5.747357e-02 -0.0578267430 -5.803834e-02
## PctTeen2Par           -5.157266e-02 -5.053026e-02 -0.0494412463 -4.825688e-02
## PctWorkMomYoungKids   -4.910554e-03 -5.132377e-03 -0.0052026986 -5.295217e-03
## PctWorkMom            -4.466202e-02 -4.717275e-02 -0.0498766950 -5.258069e-02
## NumIlleg               2.823057e-02  2.563124e-02  0.0227313449  1.982420e-02
## PctIlleg               1.098487e-01  1.121133e-01  0.1143962403  1.166034e-01
## NumImmig              -4.322566e-02 -4.594352e-02 -0.0488325861 -5.156842e-02
## PctImmigRecent         2.373749e-03  2.788576e-03  0.0032340090  3.721997e-03
## PctImmigRec5          -8.802075e-03 -9.355970e-03 -0.0098654460 -1.041260e-02
## PctImmigRec8          -4.231789e-03 -4.788298e-03 -0.0053455872 -5.895512e-03
## PctImmigRec10          7.473662e-03  7.333205e-03  0.0071745130  7.026139e-03
## PctRecentImmig         2.640637e-03  2.495769e-03  0.0022562189  2.006734e-03
## PctRecImmig5           6.750996e-03  6.780059e-03  0.0067828124  6.740898e-03
## PctRecImmig8           1.385150e-02  1.437378e-02  0.0149639004  1.552118e-02
## PctRecImmig10          1.696163e-02  1.756807e-02  0.0182702138  1.892243e-02
## PctSpeakEnglOnly       2.673312e-03  2.639964e-03  0.0024516455  2.347695e-03
## PctNotSpeakEnglWell   -8.531135e-03 -9.301521e-03 -0.0100971580 -1.093168e-02
## PctLargHouseFam        1.462366e-02  1.347250e-02  0.0122117706  1.090580e-02
## PctLargHouseOccup      6.849789e-03  5.617994e-03  0.0041693394  2.758247e-03
## PersPerOccupHous       2.101249e-02  2.251552e-02  0.0241791179  2.593596e-02
## PersPerOwnOccHous     -1.923727e-02 -2.009031e-02 -0.0209794410 -2.194643e-02
## PersPerRentOccHous     1.134517e-02  1.075949e-02  0.0101216507  9.480631e-03
## PctPersOwnOccup       -2.393154e-02 -2.485005e-02 -0.0258900131 -2.698012e-02
## PctPersDenseHous       3.995831e-02  4.142059e-02  0.0429816348  4.463720e-02
## PctHousLess3BR         2.451240e-02  2.520581e-02  0.0259894418  2.676312e-02
## MedNumBR              -2.325217e-04 -9.728788e-06  0.0002177742  4.540019e-04
## HousVacant             7.305679e-02  7.551810e-02  0.0780182572  8.062252e-02
## PctHousOccup          -5.631004e-02 -5.737414e-02 -0.0583806636 -5.932550e-02
## PctHousOwnOcc         -6.362781e-03 -6.119834e-03 -0.0060473363 -5.832915e-03
## PctVacantBoarded       5.266788e-02  5.316579e-02  0.0536981147  5.415570e-02
## PctVacMore6Mos        -2.349679e-02 -2.477352e-02 -0.0260810939 -2.741101e-02
## MedYrHousBuilt         4.771848e-03  4.207366e-03  0.0036131514  2.950055e-03
## PctHousNoPhone         1.971955e-02  1.971027e-02  0.0198442049  1.987311e-02
## PctWOFullPlumb        -2.046435e-03 -2.501578e-03 -0.0029156111 -3.324539e-03
## OwnOccLowQuart        -1.643418e-02 -1.750535e-02 -0.0187134686 -1.986543e-02
## OwnOccMedVal          -5.981595e-03 -6.304232e-03 -0.0066962701 -7.014958e-03
## OwnOccHiQuart          4.290731e-05 -5.428291e-05 -0.0001747254 -2.597409e-04
## RentLowQ              -3.318856e-02 -3.577096e-02 -0.0386943434 -4.165169e-02
## RentMedian             3.967203e-03  4.367229e-03  0.0046711903  5.077851e-03
## RentHighQ              1.014512e-02  1.095018e-02  0.0118262287  1.267835e-02
## MedRent                2.080680e-02  2.229030e-02  0.0240529829  2.577691e-02
## MedRentPctHousInc      3.339338e-02  3.425436e-02  0.0352315071  3.618581e-02
## MedOwnCostPctInc      -5.275782e-03 -6.200459e-03 -0.0070914200 -8.032563e-03
## MedOwnCostPctIncNoMtg -4.060441e-02 -4.229695e-02 -0.0439901252 -4.560534e-02
## NumInShelters          6.331065e-02  6.491107e-02  0.0666502784  6.836175e-02
## NumStreet              1.296998e-01  1.335498e-01  0.1374136829  1.411145e-01
## PctForeignBorn         1.435744e-02  1.503467e-02  0.0157969011  1.657281e-02
## PctBornSameState      -9.311055e-03 -8.926162e-03 -0.0085423638 -8.136588e-03
## PctSameHouse85         8.674903e-03  9.051257e-03  0.0094067177  9.726929e-03
## PctSameCity85          2.483382e-02  2.532326e-02  0.0258175808  2.628755e-02
## PctSameState85         9.314437e-04  1.061544e-03  0.0012505307  1.398853e-03
## LandArea               2.264129e-02  2.294627e-02  0.0233697311  2.367584e-02
## PopDens                7.039754e-03  6.715525e-03  0.0064102056  6.078755e-03
## PctUsePubTrans        -2.391051e-03 -3.349385e-03 -0.0043045220 -5.292591e-03
##                                                                              
## (Intercept)            0.4475332239  0.4541235916  0.4606683079  0.4673432074
## (Intercept)            .             .             .             .           
## state                 -0.0008235222 -0.0008308664 -0.0008375050 -0.0008436412
## fold                  -0.0014919111 -0.0015169360 -0.0015403518 -0.0015624724
## population            -0.0092535522 -0.0108466016 -0.0124250306 -0.0138453049
## householdsize          0.0196118540  0.0208721287  0.0221558882  0.0235733206
## racepctblack           0.1177312052  0.1197006283  0.1216408978  0.1235950481
## racePctWhite          -0.0846701775 -0.0848853016 -0.0849804875 -0.0849641027
## racePctAsian          -0.0181110232 -0.0186845341 -0.0191970376 -0.0196633208
## racePctHisp            0.0077993307  0.0084883339  0.0092028190  0.0099487395
## agePct12t21           -0.0082652560 -0.0075537100 -0.0067768666 -0.0056975992
## agePct12t29           -0.0593022571 -0.0626878555 -0.0662835869 -0.0702615341
## agePct16t24           -0.0143982370 -0.0141606811 -0.0139124935 -0.0137776522
## agePct65up             0.0186554173  0.0195917815  0.0205406855  0.0216178888
## numbUrban             -0.0015397846 -0.0031200973 -0.0047204245 -0.0064340098
## pctUrban               0.0331575482  0.0337163810  0.0342511801  0.0347593747
## medIncome              0.0096920244  0.0105385319  0.0114449425  0.0124718020
## pctWWage              -0.0271241664 -0.0286393610 -0.0302617100 -0.0322017065
## pctWFarmSelf           0.0059229902  0.0072049374  0.0084898036  0.0097801897
## pctWInvInc            -0.0643499737 -0.0663842300 -0.0684865261 -0.0707976343
## pctWSocSec             0.0182393184  0.0192126623  0.0201989680  0.0213695031
## pctWPubAsst            0.0119024761  0.0113033782  0.0106783569  0.0101426518
## pctWRetire            -0.0447796077 -0.0466831698 -0.0485724327 -0.0504181749
## medFamInc              0.0038778697  0.0046508676  0.0055081735  0.0065758197
## perCapInc             -0.0034111935 -0.0042204482 -0.0050754289 -0.0059616854
## whitePerCap            0.0044025267  0.0021096243 -0.0003872410 -0.0033080664
## blackPerCap           -0.0217088234 -0.0220051776 -0.0222773217 -0.0225606137
## indianPerCap          -0.0204602056 -0.0210035449 -0.0215178128 -0.0220116563
## AsianPerCap            0.0221577366  0.0223758269  0.0225806821  0.0227695633
## OtherPerCap            0.0345609751  0.0352177939  0.0358490757  0.0364436981
## HispPerCap             0.0191138296  0.0193984050  0.0196922172  0.0199882410
## NumUnderPov            0.0051062206  0.0043931600  0.0037878230  0.0032937175
## PctPopUnderPov        -0.0253495654 -0.0285186132 -0.0318247509 -0.0353758629
## PctLess9thGrade       -0.0214238264 -0.0233375509 -0.0253124894 -0.0275085912
## PctNotHSGrad           0.0173646413  0.0173675526  0.0173864668  0.0172497545
## PctBSorMore           -0.0032193363 -0.0026825355 -0.0020684301 -0.0012742858
## PctUnemployed         -0.0187628413 -0.0201548591 -0.0214772625 -0.0228722438
## PctEmploy              0.0228432716  0.0256002095  0.0285027442  0.0319261499
## PctEmplManu           -0.0232061383 -0.0237976476 -0.0244140799 -0.0250017821
## PctEmplProfServ       -0.0061692373 -0.0059161501 -0.0056395232 -0.0054358565
## PctOccupManu           0.0125867638  0.0137290720  0.0149385407  0.0161377296
## PctOccupMgmtProf      -0.0020686785 -0.0015501361 -0.0009532406 -0.0001703810
## MalePctDivorce         0.0650847262  0.0670489287  0.0691276351  0.0713999664
## MalePctNevMarr         0.0428697676  0.0448115637  0.0468279161  0.0488760958
## FemalePctDiv           0.0033252272  0.0003171443 -0.0028211807 -0.0061850486
## TotalPctDiv            0.0274004970  0.0264058780  0.0253893271  0.0241558161
## PersPerFam             0.0233857025  0.0239442010  0.0245228956  0.0251976823
## PctFam2Par            -0.0699568249 -0.0706072486 -0.0712559337 -0.0717689033
## PctKids2Par           -0.0900769024 -0.0917183292 -0.0934389436 -0.0953323544
## PctYoungKids2Par      -0.0582030563 -0.0583674839 -0.0585131543 -0.0586690582
## PctTeen2Par           -0.0469772760 -0.0456605788 -0.0443171511 -0.0429472063
## PctWorkMomYoungKids   -0.0053150833 -0.0052456947 -0.0050815689 -0.0047139253
## PctWorkMom            -0.0553768292 -0.0582831707 -0.0612918104 -0.0645185313
## NumIlleg               0.0167743644  0.0136318293  0.0104040686  0.0068530290
## PctIlleg               0.1187494708  0.1208543347  0.1229223132  0.1249553644
## NumImmig              -0.0542147619 -0.0568049991 -0.0593290217 -0.0619812489
## PctImmigRecent         0.0042349312  0.0047822372  0.0053605738  0.0059647448
## PctImmigRec5          -0.0109643004 -0.0115157543 -0.0120574103 -0.0125378515
## PctImmigRec8          -0.0064311430 -0.0069611888 -0.0074783998 -0.0079825093
## PctImmigRec10          0.0069022647  0.0067922057  0.0066907835  0.0065704810
## PctRecentImmig         0.0017529743  0.0014453482  0.0010717104  0.0005722911
## PctRecImmig5           0.0067040584  0.0066499318  0.0065565748  0.0063855152
## PctRecImmig8           0.0161085310  0.0167331943  0.0173804713  0.0180794740
## PctRecImmig10          0.0195727713  0.0202458908  0.0209357731  0.0217085579
## PctSpeakEnglOnly       0.0022264889  0.0020462601  0.0018139665  0.0014184115
## PctNotSpeakEnglWell   -0.0118573663 -0.0128228583 -0.0138202152 -0.0148755326
## PctLargHouseFam        0.0094623729  0.0079381127  0.0063629969  0.0046841250
## PctLargHouseOccup      0.0012712057 -0.0002971526 -0.0019179005 -0.0037445902
## PersPerOccupHous       0.0278604683  0.0299017112  0.0320678372  0.0344982391
## PersPerOwnOccHous     -0.0229421655 -0.0239978968 -0.0251445826 -0.0263807016
## PersPerRentOccHous     0.0087843385  0.0080414129  0.0072648451  0.0064182746
## PctPersOwnOccup       -0.0281530405 -0.0294451099 -0.0308874248 -0.0325406451
## PctPersDenseHous       0.0463962140  0.0482712894  0.0502754115  0.0524654498
## PctHousLess3BR         0.0275262857  0.0283092094  0.0291376972  0.0301286630
## MedNumBR               0.0007059003  0.0009757334  0.0012583825  0.0015450014
## HousVacant             0.0833273632  0.0861040934  0.0889601541  0.0918809244
## PctHousOccup          -0.0601995215 -0.0609900563 -0.0616997319 -0.0623270540
## PctHousOwnOcc         -0.0055737168 -0.0052898944 -0.0049949197 -0.0048774764
## PctVacantBoarded       0.0545858490  0.0549907213  0.0553745999  0.0557956456
## PctVacMore6Mos        -0.0287442719 -0.0300754997 -0.0314074583 -0.0327652147
## MedYrHousBuilt         0.0022849291  0.0016103503  0.0009212498  0.0002320453
## PctHousNoPhone         0.0199041122  0.0199648580  0.0200611081  0.0203052675
## PctWOFullPlumb        -0.0037232781 -0.0041014021 -0.0044561080 -0.0047746892
## OwnOccLowQuart        -0.0210944256 -0.0224338803 -0.0238642269 -0.0254343792
## OwnOccMedVal          -0.0072904357 -0.0075737247 -0.0078633709 -0.0082117835
## OwnOccHiQuart         -0.0002815032 -0.0002730665 -0.0002488288 -0.0002289290
## RentLowQ              -0.0447194289 -0.0479625167 -0.0514039562 -0.0552737851
## RentMedian             0.0056117252  0.0062186107  0.0068563159  0.0073419134
## RentHighQ              0.0136263741  0.0146616995  0.0157459091  0.0168779958
## MedRent                0.0276039466  0.0295836005  0.0317105275  0.0342069988
## MedRentPctHousInc      0.0370982732  0.0379938023  0.0389025224  0.0399386321
## MedOwnCostPctInc      -0.0090213597 -0.0100150917 -0.0110056456 -0.0119597067
## MedOwnCostPctIncNoMtg -0.0471932220 -0.0487523819 -0.0502724233 -0.0517768627
## NumInShelters          0.0700893445  0.0718088159  0.0735271991  0.0753873653
## NumStreet              0.1446926161  0.1481472423  0.1514684378  0.1547247499
## PctForeignBorn         0.0173588924  0.0181660445  0.0190010835  0.0199337743
## PctBornSameState      -0.0077061188 -0.0072511416 -0.0067815951 -0.0063248792
## PctSameHouse85         0.0100542037  0.0103932416  0.0107339807  0.0110583569
## PctSameCity85          0.0267186519  0.0271183593  0.0274951795  0.0278718478
## PctSameState85         0.0015126980  0.0016122693  0.0017129301  0.0018895786
## LandArea               0.0239940191  0.0243156063  0.0246245968  0.0249956003
## PopDens                0.0057159611  0.0053227394  0.0049062921  0.0045076438
## PctUsePubTrans        -0.0063013575 -0.0073254369 -0.0083625166 -0.0093953098
##                                                                              
## (Intercept)            0.4733915931  4.792953e-01  0.4853726101  4.909571e-01
## (Intercept)            .             .             .             .           
## state                 -0.0008490723 -8.533795e-04 -0.0008569344 -8.598109e-04
## fold                  -0.0015832185 -1.602138e-03 -0.0016195978 -1.635743e-03
## population            -0.0152398041 -1.666474e-02 -0.0179611304 -1.926276e-02
## householdsize          0.0248773898  2.612621e-02  0.0274841322  2.873267e-02
## racepctblack           0.1256003384  1.275931e-01  0.1296063557  1.316153e-01
## racePctWhite          -0.0849268628 -8.479739e-02 -0.0845405451 -8.422446e-02
## racePctAsian          -0.0200525389 -2.041723e-02 -0.0207560159 -2.101515e-02
## racePctHisp            0.0107794791  1.169205e-02  0.0126530542  1.365637e-02
## agePct12t21           -0.0046873181 -3.661365e-03 -0.0023369266 -1.075849e-03
## agePct12t29           -0.0741824402 -7.836406e-02 -0.0829800073 -8.754657e-02
## agePct16t24           -0.0135316291 -1.321612e-02 -0.0130225937 -1.275503e-02
## agePct65up             0.0226079863  2.363541e-02  0.0247654460  2.576758e-02
## numbUrban             -0.0080694428 -9.654546e-03 -0.0113220668 -1.293534e-02
## pctUrban               0.0352419878  3.570606e-02  0.0361508118  3.657313e-02
## medIncome              0.0134718539  1.439880e-02  0.0154029570  1.641251e-02
## pctWWage              -0.0341546348 -3.623373e-02 -0.0386754590 -4.110912e-02
## pctWFarmSelf           0.0110255261  1.227027e-02  0.0135239739  1.474070e-02
## pctWInvInc            -0.0731881943 -7.566175e-02 -0.0783456412 -8.105970e-02
## pctWSocSec             0.0225029370  2.358867e-02  0.0248577291  2.609496e-02
## pctWPubAsst            0.0096283421  9.139920e-03  0.0087482521  8.336508e-03
## pctWRetire            -0.0522160182 -5.403513e-02 -0.0558148781 -5.752050e-02
## medFamInc              0.0076469817  8.722266e-03  0.0099340813  1.111835e-02
## perCapInc             -0.0068205950 -7.762510e-03 -0.0087848174 -9.805555e-03
## whitePerCap           -0.0062591042 -9.366618e-03 -0.0129413092 -1.657178e-02
## blackPerCap           -0.0227945812 -2.300399e-02 -0.0232503483 -2.346965e-02
## indianPerCap          -0.0224678631 -2.289522e-02 -0.0233091018 -2.369228e-02
## AsianPerCap            0.0229525275  2.312276e-02  0.0232737953  2.342284e-02
## OtherPerCap            0.0370226360  3.757694e-02  0.0380922430  3.858989e-02
## HispPerCap             0.0202899054  2.060824e-02  0.0209241653  2.123658e-02
## NumUnderPov            0.0029381825  2.761692e-03  0.0028574072  3.139416e-03
## PctPopUnderPov        -0.0389403587 -4.263668e-02 -0.0465356848 -5.038656e-02
## PctLess9thGrade       -0.0295956288 -3.172631e-02 -0.0340421241 -3.622341e-02
## PctNotHSGrad           0.0172876961  1.739362e-02  0.0173802884  1.754942e-02
## PctBSorMore           -0.0004607353  4.507534e-04  0.0016054696  2.789433e-03
## PctUnemployed         -0.0240757010 -2.512214e-02 -0.0261911751 -2.706657e-02
## PctEmploy              0.0353313383  3.880404e-02  0.0427796419  4.673633e-02
## PctEmplManu           -0.0256265514 -2.630275e-02 -0.0269778877 -2.770037e-02
## PctEmplProfServ       -0.0052468107 -5.039007e-03 -0.0049124533 -4.787052e-03
## PctOccupManu           0.0173878331  1.876108e-02  0.0201632292  2.161372e-02
## PctOccupMgmtProf       0.0007450139  1.710160e-03  0.0028495244  4.112236e-03
## MalePctDivorce         0.0738342861  7.638705e-02  0.0791655915  8.207433e-02
## MalePctNevMarr         0.0510318758  5.336128e-02  0.0558067901  5.832653e-02
## FemalePctDiv          -0.0096014630 -1.311217e-02 -0.0168432928 -2.061274e-02
## TotalPctDiv            0.0230039283  2.188428e-02  0.0205724367  1.934124e-02
## PersPerFam             0.0256718003  2.617179e-02  0.0267908050  2.726137e-02
## PctFam2Par            -0.0721739830 -7.271628e-02 -0.0732538421 -7.371855e-02
## PctKids2Par           -0.0970517124 -9.881681e-02 -0.1008409999 -1.027881e-01
## PctYoungKids2Par      -0.0586862866 -5.860937e-02 -0.0585286837 -5.837007e-02
## PctTeen2Par           -0.0415465968 -4.007551e-02 -0.0385803954 -3.711393e-02
## PctWorkMomYoungKids   -0.0043518826 -3.906319e-03 -0.0032503274 -2.575269e-03
## PctWorkMom            -0.0676971052 -7.093345e-02 -0.0743881852 -7.780653e-02
## NumIlleg               0.0033386187 -3.313714e-04 -0.0043291379 -8.214291e-03
## PctIlleg               0.1268989444  1.287451e-01  0.1304806797  1.321320e-01
## NumImmig              -0.0644815232 -6.686769e-02 -0.0693717801 -7.171570e-02
## PctImmigRecent         0.0065901642  7.224108e-03  0.0078744299  8.548111e-03
## PctImmigRec5          -0.0130392748 -1.353842e-02 -0.0139871800 -1.444210e-02
## PctImmigRec8          -0.0084589864 -8.908340e-03 -0.0093453373 -9.756011e-03
## PctImmigRec10          0.0064518439  6.355753e-03  0.0062541042  6.156551e-03
## PctRecentImmig         0.0000491509 -4.773210e-04 -0.0011132947 -1.799628e-03
## PctRecImmig5           0.0061615652  5.936139e-03  0.0056593508  5.339423e-03
## PctRecImmig8           0.0187369513  1.941919e-02  0.0201841731  2.093704e-02
## PctRecImmig10          0.0224155096  2.309571e-02  0.0238510923  2.456429e-02
## PctSpeakEnglOnly       0.0011275236  8.473289e-04  0.0004043650  3.406593e-05
## PctNotSpeakEnglWell   -0.0159662497 -1.718277e-02 -0.0185210438 -1.988191e-02
## PctLargHouseFam        0.0030117651  1.213114e-03 -0.0007542696 -2.670170e-03
## PctLargHouseOccup     -0.0054937768 -7.267192e-03 -0.0092776829 -1.122783e-02
## PersPerOccupHous       0.0370488000  3.983810e-02  0.0429709371  4.616969e-02
## PersPerOwnOccHous     -0.0276995177 -2.906708e-02 -0.0304735069 -3.196125e-02
## PersPerRentOccHous     0.0055909399  4.704008e-03  0.0036894573  2.659377e-03
## PctPersOwnOccup       -0.0342123062 -3.598044e-02 -0.0379307290 -3.989682e-02
## PctPersDenseHous       0.0547325767  5.711889e-02  0.0597101484  6.236092e-02
## PctHousLess3BR         0.0310935070  3.203005e-02  0.0330827573  3.408154e-02
## MedNumBR               0.0018411875  2.153531e-03  0.0024832869  2.829833e-03
## HousVacant             0.0948423431  9.785725e-02  0.1008777945  1.038888e-01
## PctHousOccup          -0.0628855441 -6.336517e-02 -0.0637423282 -6.404939e-02
## PctHousOwnOcc         -0.0046530435 -4.344182e-03 -0.0041350271 -3.795358e-03
## PctVacantBoarded       0.0561528007  5.648321e-02  0.0568345647  5.712039e-02
## PctVacMore6Mos        -0.0341209171 -3.546421e-02 -0.0368115572 -3.813621e-02
## MedYrHousBuilt        -0.0005120525 -1.230782e-03 -0.0019053346 -2.606615e-03
## PctHousNoPhone         0.0204675147  2.062597e-02  0.0208961438  2.108154e-02
## PctWOFullPlumb        -0.0050807259 -5.378742e-03 -0.0056570403 -5.931017e-03
## OwnOccLowQuart        -0.0269579413 -2.854629e-02 -0.0303210978 -3.209955e-02
## OwnOccMedVal          -0.0084807493 -8.672420e-03 -0.0088876138 -9.039135e-03
## OwnOccHiQuart         -0.0001777473 -4.910865e-05  0.0001466103  3.913449e-04
## RentLowQ              -0.0591344971 -6.308278e-02 -0.0674029974 -7.169670e-02
## RentMedian             0.0078918697  8.586207e-03  0.0092197329  9.931009e-03
## RentHighQ              0.0179020018  1.897003e-02  0.0201320944  2.121692e-02
## MedRent                0.0366549353  3.917262e-02  0.0420966078  4.503548e-02
## MedRentPctHousInc      0.0409632523  4.193283e-02  0.0429316360  4.386534e-02
## MedOwnCostPctInc      -0.0129359414 -1.396900e-02 -0.0149934809 -1.602130e-02
## MedOwnCostPctIncNoMtg -0.0531819543 -5.453055e-02 -0.0558667071 -5.712000e-02
## NumInShelters          0.0772000863  7.900717e-02  0.0808968024  8.267047e-02
## NumStreet              0.1577831928  1.606689e-01  0.1634493970  1.660275e-01
## PctForeignBorn         0.0208988110  2.186891e-02  0.0229118172  2.397021e-02
## PctBornSameState      -0.0058776254 -5.420681e-03 -0.0049722724 -4.545697e-03
## PctSameHouse85         0.0113041405  1.153707e-02  0.0117669695  1.194684e-02
## PctSameCity85          0.0282333407  2.855919e-02  0.0288586527  2.913340e-02
## PctSameState85         0.0020454757  2.167425e-03  0.0023353027  2.480443e-03
## LandArea               0.0252413203  2.547078e-02  0.0257464927  2.592225e-02
## PopDens                0.0041097939  3.698029e-03  0.0032927263  2.880115e-03
## PctUsePubTrans        -0.0104343798 -1.148062e-02 -0.0125203031 -1.355378e-02
##                                                                              
## (Intercept)            0.4964499039  0.5019202776  0.5075149862  0.5125508572
## (Intercept)            .             .             .             .           
## state                 -0.0008618282 -0.0008632560 -0.0008642304 -0.0008648032
## fold                  -0.0016503432 -0.0016636371 -0.0016759491 -0.0016873858
## population            -0.0206046981 -0.0219273191 -0.0231209089 -0.0242602478
## householdsize          0.0299102252  0.0310494504  0.0322669160  0.0333573713
## racepctblack           0.1335989909  0.1355698071  0.1376014533  0.1396355055
## racePctWhite          -0.0837876930 -0.0832222498 -0.0825173965 -0.0817828411
## racePctAsian          -0.0212267092 -0.0213847563 -0.0214881676 -0.0215143944
## racePctHisp            0.0146988271  0.0157516126  0.0168174332  0.0179066884
## agePct12t21            0.0001912722  0.0015337734  0.0031961048  0.0048269641
## agePct12t29           -0.0923982012 -0.0975200738 -0.1031129132 -0.1085847278
## agePct16t24           -0.0124224259 -0.0120848524 -0.0118932191 -0.0116699274
## agePct65up             0.0267617948  0.0277567656  0.0288115510  0.0297517514
## numbUrban             -0.0145196410 -0.0161062017 -0.0178144933 -0.0194976863
## pctUrban               0.0369797474  0.0373689407  0.0377407045  0.0380904850
## medIncome              0.0174092381  0.0184275376  0.0195740344  0.0207028787
## pctWWage              -0.0436278615 -0.0462721563 -0.0492845199 -0.0522826781
## pctWFarmSelf           0.0159489217  0.0171457213  0.0183397313  0.0194839826
## pctWInvInc            -0.0837920582 -0.0865370845 -0.0894534389 -0.0923604052
## pctWSocSec             0.0272601229  0.0283904223  0.0296804307  0.0309367071
## pctWPubAsst            0.0079028252  0.0074303373  0.0070139825  0.0065832882
## pctWRetire            -0.0592161202 -0.0608700745 -0.0624429035 -0.0639100464
## medFamInc              0.0123355375  0.0136395967  0.0151978813  0.0167989332
## perCapInc             -0.0108986581 -0.0120358690 -0.0131756151 -0.0142201094
## whitePerCap           -0.0203740363 -0.0243880325 -0.0289301830 -0.0334932967
## blackPerCap           -0.0236759340 -0.0238757416 -0.0241193307 -0.0243307138
## indianPerCap          -0.0240499240 -0.0243856965 -0.0247114940 -0.0250096919
## AsianPerCap            0.0235625283  0.0236932907  0.0238087704  0.0239275363
## OtherPerCap            0.0390647889  0.0395154638  0.0399337070  0.0403378831
## HispPerCap             0.0215623687  0.0218999139  0.0222330505  0.0225632954
## NumUnderPov            0.0036127386  0.0043062087  0.0053254370  0.0064836175
## PctPopUnderPov        -0.0543315946 -0.0583710068 -0.0625959635 -0.0667410415
## PctLess9thGrade       -0.0384319045 -0.0406744247 -0.0430952512 -0.0453765629
## PctNotHSGrad           0.0178160533  0.0181273763  0.0183687224  0.0187636697
## PctBSorMore            0.0040551477  0.0054384154  0.0070589575  0.0086864716
## PctUnemployed         -0.0277664295 -0.0283409829 -0.0288977810 -0.0292767408
## PctEmploy              0.0507389019  0.0548605476  0.0595094382  0.0641492566
## PctEmplManu           -0.0284789155 -0.0293020226 -0.0301305190 -0.0309887897
## PctEmplProfServ       -0.0046429387 -0.0045040921 -0.0044817946 -0.0044927170
## PctOccupManu           0.0231848608  0.0248444097  0.0265331450  0.0282338366
## PctOccupMgmtProf       0.0054345220  0.0068172788  0.0084084188  0.0101103195
## MalePctDivorce         0.0850706184  0.0881613392  0.0914938878  0.0949164074
## MalePctNevMarr         0.0609988588  0.0637907623  0.0667366881  0.0697220731
## FemalePctDiv          -0.0244660879 -0.0284209369 -0.0325896265 -0.0367502051
## TotalPctDiv            0.0181368687  0.0169252419  0.0154887945  0.0141068139
## PersPerFam             0.0277490363  0.0282577719  0.0288030509  0.0291537695
## PctFam2Par            -0.0742989196 -0.0749621765 -0.0755598505 -0.0760165977
## PctKids2Par           -0.1047969527 -0.1069319341 -0.1093391174 -0.1116531207
## PctYoungKids2Par      -0.0581372506 -0.0578610231 -0.0575645612 -0.0571982281
## PctTeen2Par           -0.0356300753 -0.0341523869 -0.0326949369 -0.0313009339
## PctWorkMomYoungKids   -0.0018045940 -0.0009272338  0.0001704239  0.0012723594
## PctWorkMom            -0.0812788095 -0.0848262625 -0.0885925054 -0.0922798101
## NumIlleg              -0.0121864048 -0.0162361361 -0.0205576550 -0.0247437158
## PctIlleg               0.1336948156  0.1351492905  0.1364557220  0.1376662665
## NumImmig              -0.0739226912 -0.0760556423 -0.0782830525 -0.0803752154
## PctImmigRecent         0.0092262889  0.0099132117  0.0106112199  0.0113092820
## PctImmigRec5          -0.0148857797 -0.0153143016 -0.0156716366 -0.0160135209
## PctImmigRec8          -0.0101381159 -0.0105042364 -0.0108548572 -0.0111744670
## PctImmigRec10          0.0060842529  0.0060263064  0.0059549236  0.0058737554
## PctRecentImmig        -0.0025130337 -0.0032817231 -0.0041838466 -0.0051219481
## PctRecImmig5           0.0049994996  0.0046372492  0.0041792976  0.0036730889
## PctRecImmig8           0.0217176812  0.0225414576  0.0234453056  0.0243269534
## PctRecImmig10          0.0252566080  0.0259443239  0.0266932822  0.0273830418
## PctSpeakEnglOnly      -0.0003447081 -0.0007789766 -0.0013817572 -0.0019040116
## PctNotSpeakEnglWell   -0.0213498380 -0.0229003001 -0.0245866835 -0.0262891069
## PctLargHouseFam       -0.0046412280 -0.0066651001 -0.0088110911 -0.0108622908
## PctLargHouseOccup     -0.0131592446 -0.0150993410 -0.0172228263 -0.0192561019
## PersPerOccupHous       0.0495768861  0.0532044387  0.0572666437  0.0614284531
## PersPerOwnOccHous     -0.0335486235 -0.0352239618 -0.0369977130 -0.0388507891
## PersPerRentOccHous     0.0015483477  0.0003518920 -0.0009977493 -0.0023445742
## PctPersOwnOccup       -0.0420210008 -0.0443188177 -0.0468945963 -0.0494673327
## PctPersDenseHous       0.0651393415  0.0680646008  0.0712578471  0.0744905257
## PctHousLess3BR         0.0350558060  0.0360455645  0.0372055024  0.0383287555
## MedNumBR               0.0031940589  0.0035770859  0.0039792233  0.0043900501
## HousVacant             0.1069354590  0.1099811500  0.1130229780  0.1160093705
## PctHousOccup          -0.0642777159 -0.0644171722 -0.0644537290 -0.0644401677
## PctHousOwnOcc         -0.0033487938 -0.0028391693 -0.0024097887 -0.0019068510
## PctVacantBoarded       0.0573808312  0.0576218520  0.0578832476  0.0580944795
## PctVacMore6Mos        -0.0394350601 -0.0407044563 -0.0419697952 -0.0432031033
## MedYrHousBuilt        -0.0032698291 -0.0038976495 -0.0044790056 -0.0050858251
## PctHousNoPhone         0.0212699944  0.0214936465  0.0218273687  0.0220952873
## PctWOFullPlumb        -0.0061972958 -0.0064482314 -0.0066818065 -0.0069067357
## OwnOccLowQuart        -0.0339776832 -0.0360110159 -0.0382845381 -0.0405648538
## OwnOccMedVal          -0.0091218250 -0.0091724101 -0.0092523289 -0.0092772326
## OwnOccHiQuart          0.0007168680  0.0011184622  0.0016098250  0.0021395842
## RentLowQ              -0.0760846401 -0.0806078847 -0.0855292479 -0.0903742113
## RentMedian             0.0107643103  0.0116683678  0.0124399166  0.0132010455
## RentHighQ              0.0223241477  0.0234534466  0.0245998225  0.0255751589
## MedRent                0.0480747073  0.0512725914  0.0549453767  0.0586361941
## MedRentPctHousInc      0.0447340373  0.0455604135  0.0464303599  0.0472712189
## MedOwnCostPctInc      -0.0170839672 -0.0181467101 -0.0191770754 -0.0201829153
## MedOwnCostPctIncNoMtg -0.0583187910 -0.0594709075 -0.0606075898 -0.0616591344
## NumInShelters          0.0843976159  0.0860780706  0.0878241412  0.0894793018
## NumStreet              0.1684213612  0.1706476437  0.1727502734  0.1746737146
## PctForeignBorn         0.0250235357  0.0260779489  0.0272242788  0.0284051929
## PctBornSameState      -0.0041174426 -0.0036795733 -0.0032556120 -0.0028632297
## PctSameHouse85         0.0121262282  0.0123051390  0.0124791535  0.0125787732
## PctSameCity85          0.0293684294  0.0295645418  0.0297326139  0.0298893095
## PctSameState85         0.0025999721  0.0027123932  0.0028913944  0.0030719327
## LandArea               0.0260780646  0.0262119145  0.0263607588  0.0264227641
## PopDens                0.0024507745  0.0020058668  0.0015699396  0.0011476657
## PctUsePubTrans        -0.0145889960 -0.0156251270 -0.0166579305 -0.0176715338
ridge.mod$lambda
##   [1] 171.99845173 156.71858334 142.79613634 130.11052116 118.55186107
##   [6] 108.02004049  98.42383782  89.68013534  81.71319929  74.45402388
##  [11]  67.83973361  61.81303866  56.32173867  51.31827064  46.75929692
##  [16]  42.60532986  38.82038978  35.37169335  32.22936961  29.36620124
##  [21]  26.75738886  24.38033618  22.21445431  20.24098342  18.44283024
##  [26]  16.80442003  15.31156167  13.95132474  12.71192750  11.58263490
##  [31]  10.55366554   9.61610698   8.76183854   7.98346095   7.27423229
##  [36]   6.62800954   6.03919544   5.50268996   5.01384615   4.56842988
##  [41]   4.16258316   3.79279074   3.45584967   3.14884151   2.86910711
##  [46]   2.61422354   2.38198313   2.17037431   1.97756424   1.80188288
##  [51]   1.64180857   1.49595483   1.36305832   1.24196798   1.13163497
##  [56]   1.03110363   0.93950323   0.85604035   0.77999209   0.71069974
##  [61]   0.64756313   0.59003541   0.53761829   0.48985776   0.44634015
##  [66]   0.40668852   0.37055943   0.33763995   0.30764495   0.28031462
##  [71]   0.25541224   0.23272212   0.21204773   0.19321000   0.17604575
##  [76]   0.16040633   0.14615628   0.13317216   0.12134151   0.11056186
##  [81]   0.10073985   0.09179040   0.08363599   0.07620600   0.06943607
##  [86]   0.06326756   0.05764705   0.05252584   0.04785959   0.04360788
##  [91]   0.03973387   0.03620403   0.03298776   0.03005722   0.02738702
##  [96]   0.02495403   0.02273718   0.02071727   0.01887681   0.01719985
plot(ridge.mod,"lambda", label=TRUE)

ridge.mod$lambda[5]
## [1] 118.5519
log(ridge.mod$lambda[5])
## [1] 4.775351
coef(ridge.mod)[,5]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##           (Intercept)           (Intercept)                 state 
##          2.408608e-01          0.000000e+00         -5.809497e-06 
##                  fold            population         householdsize 
##         -5.026316e-06          1.277161e-03         -9.759961e-05 
##          racepctblack          racePctWhite          racePctAsian 
##          1.107236e-03         -1.242253e-03          8.393138e-05 
##           racePctHisp           agePct12t21           agePct12t29 
##          5.486080e-04          1.552188e-04          4.517798e-04 
##           agePct16t24            agePct65up             numbUrban 
##          2.476580e-04          1.645788e-04          1.251180e-03 
##              pctUrban             medIncome              pctWWage 
##          8.780469e-05         -8.753072e-04         -7.284557e-04 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##         -3.371917e-04         -1.414723e-03          2.944539e-04 
##           pctWPubAsst            pctWRetire             medFamInc 
##          1.131630e-03         -2.578268e-04         -9.579953e-04 
##             perCapInc           whitePerCap           blackPerCap 
##         -7.914889e-04         -4.703871e-04         -6.929404e-04 
##          indianPerCap           AsianPerCap           OtherPerCap 
##         -2.352204e-04         -3.374829e-04         -2.782774e-04 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##         -5.699369e-04          1.540775e-03          9.945026e-04 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##          8.358293e-04          1.039578e-03         -6.504941e-04 
##         PctUnemployed             PctEmploy           PctEmplManu 
##          1.087290e-03         -8.275196e-04         -1.028824e-04 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##         -1.783911e-04          6.407005e-04         -7.858235e-04 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##          1.268924e-03          7.606498e-04          1.397903e-03 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##          1.326163e-03          3.984205e-04         -1.539690e-03 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##         -1.575876e-03         -1.338781e-03         -1.522440e-03 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##         -5.901190e-05         -3.782991e-04          1.915348e-03 
##              PctIlleg              NumImmig        PctImmigRecent 
##          1.418760e-03          1.483465e-03          3.398310e-04 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##          4.446755e-04          5.368248e-04          6.532797e-04 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##          4.293147e-04          4.608521e-04          4.704452e-04 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##          4.952038e-04         -4.644079e-04          5.949591e-04 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##          8.552199e-04          6.766766e-04         -1.021186e-04 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##         -3.469782e-04          5.738387e-04         -1.164554e-03 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##          9.447383e-04          1.201214e-03         -6.101935e-04 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##          1.239583e-03         -7.251163e-04         -1.108661e-03 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##          9.785901e-04          4.547703e-05         -2.051449e-04 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##          8.773115e-04          7.700253e-04         -4.006782e-04 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##         -3.502443e-04         -3.094050e-04         -4.929996e-04 
##            RentMedian             RentHighQ               MedRent 
##         -4.907352e-04         -3.989263e-04         -4.787833e-04 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##          8.430518e-04          1.587122e-04          1.221550e-04 
##         NumInShelters             NumStreet        PctForeignBorn 
##          1.618177e-03          1.501186e-03          3.717099e-04 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##         -1.728715e-04         -3.705701e-04          1.697735e-04 
##        PctSameState85              LandArea               PopDens 
##         -4.433635e-05          7.964360e-04          6.103165e-04 
##        PctUsePubTrans 
##          3.023410e-04
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(ridge.mod$lambda[5]), col="blue", lwd=4, lty=3)

log(ridge.mod$lambda[70])
## [1] -1.271843
coef(ridge.mod)[,70]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##           (Intercept)           (Intercept)                 state 
##          3.297768e-01          0.000000e+00         -5.472795e-04 
##                  fold            population         householdsize 
##         -7.497081e-04          1.871996e-02          1.720520e-03 
##          racepctblack          racePctWhite          racePctAsian 
##          7.665504e-02         -6.563081e-02         -3.969969e-03 
##           racePctHisp           agePct12t21           agePct12t29 
##          3.795609e-03         -1.305775e-02         -2.168276e-02 
##           agePct16t24            agePct65up             numbUrban 
##         -1.430345e-02          6.453711e-03          2.367583e-02 
##              pctUrban             medIncome              pctWWage 
##          1.906802e-02         -2.247983e-03         -1.479235e-02 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##         -1.242855e-02         -4.082123e-02          6.320126e-03 
##           pctWPubAsst            pctWRetire             medFamInc 
##          2.252459e-02         -1.576393e-02         -6.102173e-03 
##             perCapInc           whitePerCap           blackPerCap 
##          4.657242e-04          1.748806e-02         -1.272751e-02 
##          indianPerCap           AsianPerCap           OtherPerCap 
##         -7.070248e-03          1.377703e-02          1.804893e-02 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##          1.194161e-02          2.703722e-02          1.057418e-02 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##          3.032582e-03          1.848870e-02         -5.999242e-03 
##         PctUnemployed             PctEmploy           PctEmplManu 
##          9.339117e-03         -6.589839e-03         -1.434873e-02 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##         -5.967509e-03          3.306342e-03         -4.680411e-03 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##          4.551428e-02          2.036452e-02          3.723954e-02 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##          4.000917e-02          1.535591e-02         -5.687271e-02 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##         -6.413976e-02         -4.869122e-02         -5.546508e-02 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##         -7.914507e-05         -1.853212e-02          5.122660e-02 
##              PctIlleg              NumImmig        PctImmigRecent 
##          7.553013e-02         -3.612684e-03          2.062392e-04 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##         -1.226401e-03          3.278973e-03          9.747493e-03 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##          3.543828e-03          5.846424e-03          8.704961e-03 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##          1.052167e-02          2.634126e-04          4.290006e-05 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##          2.215884e-02          1.620209e-02          7.399569e-03 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##         -1.164483e-02          1.583568e-02         -1.871081e-02 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##          2.614299e-02          1.849316e-02         -3.591169e-03 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##          4.765482e-02         -3.759136e-02         -1.097143e-02 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##          4.263753e-02         -8.488120e-03          6.287736e-03 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##          2.002779e-02          6.246714e-03         -5.641226e-03 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##         -1.649954e-03          1.638952e-03         -1.027964e-02 
##            RentMedian             RentHighQ               MedRent 
##          8.802502e-04          2.774674e-03          7.142587e-03 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##          2.452740e-02          5.472063e-03         -1.529272e-02 
##         NumInShelters             NumStreet        PctForeignBorn 
##          4.693200e-02          7.444839e-02          8.125758e-03 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##         -1.155977e-02          2.406200e-03          1.647930e-02 
##        PctSameState85              LandArea               PopDens 
##         -7.922816e-04          2.077187e-02          1.021178e-02 
##        PctUsePubTrans 
##          7.968234e-03
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(ridge.mod$lambda[70]), col="blue", lwd=4, lty=3)

datosx<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]
pred<-predict(ridge.mod,s=ridge.mod$lambda[5],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 0.0518556
## 
## $raiz.error.cuadratico
## [1] 0.233776
## 
## $error.relativo
## [1] 0.730497
## 
## $correlacion
## [1] 0.7288325
pred<-predict(ridge.mod,s=ridge.mod$lambda[70],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 0.01917118
## 
## $raiz.error.cuadratico
## [1] 0.1421433
## 
## $error.relativo
## [1] 0.4074684
## 
## $correlacion
## [1] 0.8078744
# Usando validación cruzada para determinar el mejor Lambda
sal.cv<-cv.glmnet(x,y,alpha=0)
plot(sal.cv)

mejor.lambda<-sal.cv$lambda.min
mejor.lambda
## [1] 0.01719985
log(mejor.lambda)
## [1] -4.062855
coef(ridge.mod)[,which(ridge.mod$lambda==mejor.lambda)]
##           (Intercept)           (Intercept)                 state 
##          0.5125508572          0.0000000000         -0.0008648032 
##                  fold            population         householdsize 
##         -0.0016873858         -0.0242602478          0.0333573713 
##          racepctblack          racePctWhite          racePctAsian 
##          0.1396355055         -0.0817828411         -0.0215143944 
##           racePctHisp           agePct12t21           agePct12t29 
##          0.0179066884          0.0048269641         -0.1085847278 
##           agePct16t24            agePct65up             numbUrban 
##         -0.0116699274          0.0297517514         -0.0194976863 
##              pctUrban             medIncome              pctWWage 
##          0.0380904850          0.0207028787         -0.0522826781 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##          0.0194839826         -0.0923604052          0.0309367071 
##           pctWPubAsst            pctWRetire             medFamInc 
##          0.0065832882         -0.0639100464          0.0167989332 
##             perCapInc           whitePerCap           blackPerCap 
##         -0.0142201094         -0.0334932967         -0.0243307138 
##          indianPerCap           AsianPerCap           OtherPerCap 
##         -0.0250096919          0.0239275363          0.0403378831 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##          0.0225632954          0.0064836175         -0.0667410415 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##         -0.0453765629          0.0187636697          0.0086864716 
##         PctUnemployed             PctEmploy           PctEmplManu 
##         -0.0292767408          0.0641492566         -0.0309887897 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##         -0.0044927170          0.0282338366          0.0101103195 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##          0.0949164074          0.0697220731         -0.0367502051 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##          0.0141068139          0.0291537695         -0.0760165977 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##         -0.1116531207         -0.0571982281         -0.0313009339 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##          0.0012723594         -0.0922798101         -0.0247437158 
##              PctIlleg              NumImmig        PctImmigRecent 
##          0.1376662665         -0.0803752154          0.0113092820 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##         -0.0160135209         -0.0111744670          0.0058737554 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##         -0.0051219481          0.0036730889          0.0243269534 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##          0.0273830418         -0.0019040116         -0.0262891069 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##         -0.0108622908         -0.0192561019          0.0614284531 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##         -0.0388507891         -0.0023445742         -0.0494673327 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##          0.0744905257          0.0383287555          0.0043900501 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##          0.1160093705         -0.0644401677         -0.0019068510 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##          0.0580944795         -0.0432031033         -0.0050858251 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##          0.0220952873         -0.0069067357         -0.0405648538 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##         -0.0092772326          0.0021395842         -0.0903742113 
##            RentMedian             RentHighQ               MedRent 
##          0.0132010455          0.0255751589          0.0586361941 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##          0.0472712189         -0.0201829153         -0.0616591344 
##         NumInShelters             NumStreet        PctForeignBorn 
##          0.0894793018          0.1746737146          0.0284051929 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##         -0.0028632297          0.0125787732          0.0298893095 
##        PctSameState85              LandArea               PopDens 
##          0.0030719327          0.0264227641          0.0011476657 
##        PctUsePubTrans 
##         -0.0176715338
plot(ridge.mod,"lambda", label=TRUE)
abline(v = log(mejor.lambda), col="blue", lwd=4, lty=3)

pred<-predict(ridge.mod,s=mejor.lambda,newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.ridge <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.ridge
## $error.cuadratico
## [1] 0.01720385
## 
## $raiz.error.cuadratico
## [1] 0.1346526
## 
## $error.relativo
## [1] 0.3845009
## 
## $correlacion
## [1] 0.8265301

###LASSO

# Debemos eliminar la columna 1
x<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]
head(x)
##   (Intercept) state fold population householdsize racepctblack racePctWhite
## 1           1     8    1       0.19          0.33         0.02         0.90
## 2           1    53    1       0.00          0.16         0.12         0.74
## 3           1    24    1       0.00          0.42         0.49         0.56
## 4           1    34    1       0.04          0.77         1.00         0.08
## 5           1    42    1       0.01          0.55         0.02         0.95
## 6           1     6    1       0.02          0.28         0.06         0.54
##   racePctAsian racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up
## 1         0.12        0.17        0.34        0.47        0.29       0.32
## 2         0.45        0.07        0.26        0.59        0.35       0.27
## 3         0.17        0.04        0.39        0.47        0.28       0.32
## 4         0.12        0.10        0.51        0.50        0.34       0.21
## 5         0.09        0.05        0.38        0.38        0.23       0.36
## 6         1.00        0.25        0.31        0.48        0.27       0.37
##   numbUrban pctUrban medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec
## 1      0.20      1.0      0.37     0.72         0.34       0.60       0.29
## 2      0.02      1.0      0.31     0.72         0.11       0.45       0.25
## 3      0.00      0.0      0.30     0.58         0.19       0.39       0.38
## 4      0.06      1.0      0.58     0.89         0.21       0.43       0.36
## 5      0.02      0.9      0.50     0.72         0.16       0.68       0.44
## 6      0.04      1.0      0.52     0.68         0.20       0.61       0.28
##   pctWPubAsst pctWRetire medFamInc perCapInc whitePerCap blackPerCap
## 1        0.15       0.43      0.39      0.40        0.39        0.32
## 2        0.29       0.39      0.29      0.37        0.38        0.33
## 3        0.40       0.84      0.28      0.27        0.29        0.27
## 4        0.20       0.82      0.51      0.36        0.40        0.39
## 5        0.11       0.71      0.46      0.43        0.41        0.28
## 6        0.15       0.25      0.62      0.72        0.76        0.77
##   indianPerCap AsianPerCap OtherPerCap HispPerCap NumUnderPov PctPopUnderPov
## 1         0.27        0.27        0.36       0.41        0.08           0.19
## 2         0.16        0.30        0.22       0.35        0.01           0.24
## 3         0.07        0.29        0.28       0.39        0.01           0.27
## 4         0.16        0.25        0.36       0.44        0.01           0.10
## 5         0.00        0.74        0.51       0.48        0.00           0.06
## 6         0.28        0.52        0.48       0.60        0.01           0.12
##   PctLess9thGrade PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu
## 1            0.10         0.18        0.48          0.27      0.68        0.23
## 2            0.14         0.24        0.30          0.27      0.73        0.57
## 3            0.27         0.43        0.19          0.36      0.58        0.32
## 4            0.09         0.25        0.31          0.33      0.71        0.36
## 5            0.25         0.30        0.33          0.12      0.65        0.67
## 6            0.13         0.12        0.80          0.10      0.65        0.19
##   PctEmplProfServ PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr
## 1            0.41         0.25             0.52           0.68           0.40
## 2            0.15         0.42             0.36           1.00           0.63
## 3            0.29         0.49             0.32           0.63           0.41
## 4            0.45         0.37             0.39           0.34           0.45
## 5            0.38         0.42             0.46           0.22           0.27
## 6            0.77         0.06             0.91           0.49           0.57
##   FemalePctDiv TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par
## 1         0.75        0.75       0.35       0.55        0.59             0.61
## 2         0.91        1.00       0.29       0.43        0.47             0.60
## 3         0.71        0.70       0.45       0.42        0.44             0.43
## 4         0.49        0.44       0.75       0.65        0.54             0.83
## 5         0.20        0.21       0.51       0.91        0.91             0.89
## 6         0.61        0.58       0.44       0.62        0.69             0.87
##   PctTeen2Par PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig
## 1        0.56                0.74       0.76     0.04     0.14     0.03
## 2        0.39                0.46       0.53     0.00     0.24     0.01
## 3        0.43                0.71       0.67     0.01     0.46     0.00
## 4        0.65                0.85       0.86     0.03     0.33     0.02
## 5        0.85                0.40       0.60     0.00     0.06     0.00
## 6        0.53                0.30       0.43     0.00     0.11     0.04
##   PctImmigRecent PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig
## 1           0.24         0.27         0.37          0.39           0.07
## 2           0.52         0.62         0.64          0.63           0.25
## 3           0.07         0.06         0.15          0.19           0.02
## 4           0.11         0.20         0.30          0.31           0.05
## 5           0.03         0.07         0.20          0.27           0.01
## 6           0.30         0.35         0.43          0.47           0.50
##   PctRecImmig5 PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## 1         0.07         0.08          0.08             0.89                0.06
## 2         0.27         0.25          0.23             0.84                0.10
## 3         0.02         0.04          0.05             0.88                0.04
## 4         0.08         0.11          0.11             0.81                0.08
## 5         0.02         0.04          0.05             0.88                0.05
## 6         0.50         0.56          0.57             0.45                0.28
##   PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## 1            0.14              0.13             0.33              0.39
## 2            0.16              0.10             0.17              0.29
## 3            0.20              0.20             0.46              0.52
## 4            0.56              0.62             0.85              0.77
## 5            0.16              0.19             0.59              0.60
## 6            0.25              0.19             0.29              0.53
##   PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
## 1               0.28            0.55             0.09           0.51      0.5
## 2               0.17            0.26             0.20           0.82      0.0
## 3               0.43            0.42             0.15           0.51      0.5
## 4               1.00            0.94             0.12           0.01      0.5
## 5               0.37            0.89             0.02           0.19      0.5
## 6               0.18            0.39             0.26           0.73      0.0
##   HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
## 1       0.21         0.71          0.52             0.05           0.26
## 2       0.02         0.79          0.24             0.02           0.25
## 3       0.01         0.86          0.41             0.29           0.30
## 4       0.01         0.97          0.96             0.60           0.47
## 5       0.01         0.89          0.87             0.04           0.55
## 6       0.02         0.84          0.30             0.16           0.28
##   MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
## 1           0.65           0.14           0.06           0.22         0.19
## 2           0.65           0.16           0.00           0.21         0.20
## 3           0.52           0.47           0.45           0.18         0.17
## 4           0.52           0.11           0.11           0.24         0.21
## 5           0.73           0.05           0.14           0.31         0.31
## 6           0.25           0.02           0.05           0.94         1.00
##   OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
## 1          0.18     0.36       0.35      0.38    0.34              0.38
## 2          0.21     0.42       0.38      0.40    0.37              0.29
## 3          0.16     0.27       0.29      0.27    0.31              0.48
## 4          0.19     0.75       0.70      0.77    0.89              0.63
## 5          0.30     0.40       0.36      0.38    0.38              0.22
## 6          1.00     0.67       0.63      0.68    0.62              0.47
##   MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
## 1             0.46                  0.25          0.04         0           0.12
## 2             0.32                  0.18          0.00         0           0.21
## 3             0.39                  0.28          0.00         0           0.14
## 4             0.51                  0.47          0.00         0           0.19
## 5             0.51                  0.21          0.00         0           0.11
## 6             0.59                  0.11          0.00         0           0.70
##   PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
## 1             0.42           0.50          0.51           0.64     0.12    0.26
## 2             0.50           0.34          0.60           0.52     0.02    0.12
## 3             0.49           0.54          0.67           0.56     0.01    0.21
## 4             0.30           0.73          0.64           0.65     0.02    0.39
## 5             0.72           0.64          0.61           0.53     0.04    0.09
## 6             0.42           0.49          0.73           0.64     0.01    0.58
##   PctUsePubTrans
## 1           0.20
## 2           0.45
## 3           0.02
## 4           0.28
## 5           0.02
## 6           0.10
# La siguiente instrucción construye la variable a predecir
y<-datos$ViolentCrimesPerPop
library(glmnet)

lasso.mod<-glmnet(x,y,alpha=1) 
dim(coef(lasso.mod))
## [1] 103 100
coef(lasso.mod)
## 103 x 100 sparse Matrix of class "dgCMatrix"
##    [[ suppressing 100 column names 's0', 's1', 's2' ... ]]
##                                                                               
## (Intercept)           0.2379789  0.25591869  0.27024238  0.28325558  0.2951769
## (Intercept)           .          .           .           .           .        
## state                 .          .           .           .           .        
## fold                  .          .           .           .           .        
## population            .          .           .           .           .        
## householdsize         .          .           .           .           .        
## racepctblack          .          .           .           .           .        
## racePctWhite          .          .           .           .           .        
## racePctAsian          .          .           .           .           .        
## racePctHisp           .          .           .           .           .        
## agePct12t21           .          .           .           .           .        
## agePct12t29           .          .           .           .           .        
## agePct16t24           .          .           .           .           .        
## agePct65up            .          .           .           .           .        
## numbUrban             .          .           .           .           .        
## pctUrban              .          .           .           .           .        
## medIncome             .          .           .           .           .        
## pctWWage              .          .           .           .           .        
## pctWFarmSelf          .          .           .           .           .        
## pctWInvInc            .          .           .           .           .        
## pctWSocSec            .          .           .           .           .        
## pctWPubAsst           .          .           .           .           .        
## pctWRetire            .          .           .           .           .        
## medFamInc             .          .           .           .           .        
## perCapInc             .          .           .           .           .        
## whitePerCap           .          .           .           .           .        
## blackPerCap           .          .           .           .           .        
## indianPerCap          .          .           .           .           .        
## AsianPerCap           .          .           .           .           .        
## OtherPerCap           .          .           .           .           .        
## HispPerCap            .          .           .           .           .        
## NumUnderPov           .          .           .           .           .        
## PctPopUnderPov        .          .           .           .           .        
## PctLess9thGrade       .          .           .           .           .        
## PctNotHSGrad          .          .           .           .           .        
## PctBSorMore           .          .           .           .           .        
## PctUnemployed         .          .           .           .           .        
## PctEmploy             .          .           .           .           .        
## PctEmplManu           .          .           .           .           .        
## PctEmplProfServ       .          .           .           .           .        
## PctOccupManu          .          .           .           .           .        
## PctOccupMgmtProf      .          .           .           .           .        
## MalePctDivorce        .          .           .           .           .        
## MalePctNevMarr        .          .           .           .           .        
## FemalePctDiv          .          .           .           .           .        
## TotalPctDiv           .          .           .           .           .        
## PersPerFam            .          .           .           .           .        
## PctFam2Par            .          .           .           .           .        
## PctKids2Par           .         -0.04220914 -0.07832201 -0.11118010 -0.1411978
## PctYoungKids2Par      .          .           .           .           .        
## PctTeen2Par           .          .           .           .           .        
## PctWorkMomYoungKids   .          .           .           .           .        
## PctWorkMom            .          .           .           .           .        
## NumIlleg              .          .           .           .           .        
## PctIlleg              .          0.03303125  0.06539192  0.09491414  0.1217523
## NumImmig              .          .           .           .           .        
## PctImmigRecent        .          .           .           .           .        
## PctImmigRec5          .          .           .           .           .        
## PctImmigRec8          .          .           .           .           .        
## PctImmigRec10         .          .           .           .           .        
## PctRecentImmig        .          .           .           .           .        
## PctRecImmig5          .          .           .           .           .        
## PctRecImmig8          .          .           .           .           .        
## PctRecImmig10         .          .           .           .           .        
## PctSpeakEnglOnly      .          .           .           .           .        
## PctNotSpeakEnglWell   .          .           .           .           .        
## PctLargHouseFam       .          .           .           .           .        
## PctLargHouseOccup     .          .           .           .           .        
## PersPerOccupHous      .          .           .           .           .        
## PersPerOwnOccHous     .          .           .           .           .        
## PersPerRentOccHous    .          .           .           .           .        
## PctPersOwnOccup       .          .           .           .           .        
## PctPersDenseHous      .          .           .           .           .        
## PctHousLess3BR        .          .           .           .           .        
## MedNumBR              .          .           .           .           .        
## HousVacant            .          .           .           .           .        
## PctHousOccup          .          .           .           .           .        
## PctHousOwnOcc         .          .           .           .           .        
## PctVacantBoarded      .          .           .           .           .        
## PctVacMore6Mos        .          .           .           .           .        
## MedYrHousBuilt        .          .           .           .           .        
## PctHousNoPhone        .          .           .           .           .        
## PctWOFullPlumb        .          .           .           .           .        
## OwnOccLowQuart        .          .           .           .           .        
## OwnOccMedVal          .          .           .           .           .        
## OwnOccHiQuart         .          .           .           .           .        
## RentLowQ              .          .           .           .           .        
## RentMedian            .          .           .           .           .        
## RentHighQ             .          .           .           .           .        
## MedRent               .          .           .           .           .        
## MedRentPctHousInc     .          .           .           .           .        
## MedOwnCostPctInc      .          .           .           .           .        
## MedOwnCostPctIncNoMtg .          .           .           .           .        
## NumInShelters         .          .           .           .           .        
## NumStreet             .          .           .           .           .        
## PctForeignBorn        .          .           .           .           .        
## PctBornSameState      .          .           .           .           .        
## PctSameHouse85        .          .           .           .           .        
## PctSameCity85         .          .           .           .           .        
## PctSameState85        .          .           .           .           .        
## LandArea              .          .           .           .           .        
## PopDens               .          .           .           .           .        
## PctUsePubTrans        .          .           .           .           .        
##                                                                       
## (Intercept)            0.3068252092  0.33732688  0.3652837  0.39056495
## (Intercept)            .             .           .          .         
## state                  .             .           .          .         
## fold                   .             .           .          .         
## population             .             .           .          .         
## householdsize          .             .           .          .         
## racepctblack           .             .           .          .         
## racePctWhite          -0.0007737576 -0.02244763 -0.0421901 -0.06015051
## racePctAsian           .             .           .          .         
## racePctHisp            .             .           .          .         
## agePct12t21            .             .           .          .         
## agePct12t29            .             .           .          .         
## agePct16t24            .             .           .          .         
## agePct65up             .             .           .          .         
## numbUrban              .             .           .          .         
## pctUrban               .             .           .          .         
## medIncome              .             .           .          .         
## pctWWage               .             .           .          .         
## pctWFarmSelf           .             .           .          .         
## pctWInvInc             .             .           .          .         
## pctWSocSec             .             .           .          .         
## pctWPubAsst            .             .           .          .         
## pctWRetire             .             .           .          .         
## medFamInc              .             .           .          .         
## perCapInc              .             .           .          .         
## whitePerCap            .             .           .          .         
## blackPerCap            .             .           .          .         
## indianPerCap           .             .           .          .         
## AsianPerCap            .             .           .          .         
## OtherPerCap            .             .           .          .         
## HispPerCap             .             .           .          .         
## NumUnderPov            .             .           .          .         
## PctPopUnderPov         .             .           .          .         
## PctLess9thGrade        .             .           .          .         
## PctNotHSGrad           .             .           .          .         
## PctBSorMore            .             .           .          .         
## PctUnemployed          .             .           .          .         
## PctEmploy              .             .           .          .         
## PctEmplManu            .             .           .          .         
## PctEmplProfServ        .             .           .          .         
## PctOccupManu           .             .           .          .         
## PctOccupMgmtProf       .             .           .          .         
## MalePctDivorce         .             .           .          .         
## MalePctNevMarr         .             .           .          .         
## FemalePctDiv           .             .           .          .         
## TotalPctDiv            .             .           .          .         
## PersPerFam             .             .           .          .         
## PctFam2Par             .             .           .          .         
## PctKids2Par           -0.1685953401 -0.19308543 -0.2156090 -0.23592973
## PctYoungKids2Par       .             .           .          .         
## PctTeen2Par            .             .           .          .         
## PctWorkMomYoungKids    .             .           .          .         
## PctWorkMom             .             .           .          .         
## NumIlleg               .             .           .          .         
## PctIlleg               0.1455100960  0.14964707  0.1532583  0.15673045
## NumImmig               .             .           .          .         
## PctImmigRecent         .             .           .          .         
## PctImmigRec5           .             .           .          .         
## PctImmigRec8           .             .           .          .         
## PctImmigRec10          .             .           .          .         
## PctRecentImmig         .             .           .          .         
## PctRecImmig5           .             .           .          .         
## PctRecImmig8           .             .           .          .         
## PctRecImmig10          .             .           .          .         
## PctSpeakEnglOnly       .             .           .          .         
## PctNotSpeakEnglWell    .             .           .          .         
## PctLargHouseFam        .             .           .          .         
## PctLargHouseOccup      .             .           .          .         
## PersPerOccupHous       .             .           .          .         
## PersPerOwnOccHous      .             .           .          .         
## PersPerRentOccHous     .             .           .          .         
## PctPersOwnOccup        .             .           .          .         
## PctPersDenseHous       .             .           .          .         
## PctHousLess3BR         .             .           .          .         
## MedNumBR               .             .           .          .         
## HousVacant             .             .           .          .         
## PctHousOccup           .             .           .          .         
## PctHousOwnOcc          .             .           .          .         
## PctVacantBoarded       .             .           .          .         
## PctVacMore6Mos         .             .           .          .         
## MedYrHousBuilt         .             .           .          .         
## PctHousNoPhone         .             .           .          .         
## PctWOFullPlumb         .             .           .          .         
## OwnOccLowQuart         .             .           .          .         
## OwnOccMedVal           .             .           .          .         
## OwnOccHiQuart          .             .           .          .         
## RentLowQ               .             .           .          .         
## RentMedian             .             .           .          .         
## RentHighQ              .             .           .          .         
## MedRent                .             .           .          .         
## MedRentPctHousInc      .             .           .          .         
## MedOwnCostPctInc       .             .           .          .         
## MedOwnCostPctIncNoMtg  .             .           .          .         
## NumInShelters          .             .           .          .         
## NumStreet              .             .           .          .         
## PctForeignBorn         .             .           .          .         
## PctBornSameState       .             .           .          .         
## PctSameHouse85         .             .           .          .         
## PctSameCity85          .             .           .          .         
## PctSameState85         .             .           .          .         
## LandArea               .             .           .          .         
## PopDens                .             .           .          .         
## PctUsePubTrans         .             .           .          .         
##                                                                                
## (Intercept)            0.41380568  0.43477899  0.4538809  0.4714914  0.48417716
## (Intercept)            .           .           .          .          .         
## state                  .           .           .          .          .         
## fold                   .           .           .          .          .         
## population             .           .           .          .          .         
## householdsize          .           .           .          .          .         
## racepctblack           .           .           .          .          .         
## racePctWhite          -0.07655236 -0.09146017 -0.1050436 -0.1174644 -0.12842509
## racePctAsian           .           .           .          .          .         
## racePctHisp            .           .           .          .          .         
## agePct12t21            .           .           .          .          .         
## agePct12t29            .           .           .          .          .         
## agePct16t24            .           .           .          .          .         
## agePct65up             .           .           .          .          .         
## numbUrban              .           .           .          .          .         
## pctUrban               .           .           .          .          .         
## medIncome              .           .           .          .          .         
## pctWWage               .           .           .          .          .         
## pctWFarmSelf           .           .           .          .          .         
## pctWInvInc             .           .           .          .          .         
## pctWSocSec             .           .           .          .          .         
## pctWPubAsst            .           .           .          .          .         
## pctWRetire             .           .           .          .          .         
## medFamInc              .           .           .          .          .         
## perCapInc              .           .           .          .          .         
## whitePerCap            .           .           .          .          .         
## blackPerCap            .           .           .          .          .         
## indianPerCap           .           .           .          .          .         
## AsianPerCap            .           .           .          .          .         
## OtherPerCap            .           .           .          .          .         
## HispPerCap             .           .           .          .          .         
## NumUnderPov            .           .           .          .          .         
## PctPopUnderPov         .           .           .          .          .         
## PctLess9thGrade        .           .           .          .          .         
## PctNotHSGrad           .           .           .          .          .         
## PctBSorMore            .           .           .          .          .         
## PctUnemployed          .           .           .          .          .         
## PctEmploy              .           .           .          .          .         
## PctEmplManu            .           .           .          .          .         
## PctEmplProfServ        .           .           .          .          .         
## PctOccupManu           .           .           .          .          .         
## PctOccupMgmtProf       .           .           .          .          .         
## MalePctDivorce         .           .           .          .          .         
## MalePctNevMarr         .           .           .          .          .         
## FemalePctDiv           .           .           .          .          .         
## TotalPctDiv            .           .           .          .          .         
## PersPerFam             .           .           .          .          .         
## PctFam2Par             .           .           .          .          .         
## PctKids2Par           -0.25465314 -0.27150846 -0.2868564 -0.3010406 -0.31059317
## PctYoungKids2Par       .           .           .          .          .         
## PctTeen2Par            .           .           .          .          .         
## PctWorkMomYoungKids    .           .           .          .          .         
## PctWorkMom             .           .           .          .          .         
## NumIlleg               .           .           .          .          .         
## PctIlleg               0.15970022  0.16259757  0.1652454  0.1674643  0.16917573
## NumImmig               .           .           .          .          .         
## PctImmigRecent         .           .           .          .          .         
## PctImmigRec5           .           .           .          .          .         
## PctImmigRec8           .           .           .          .          .         
## PctImmigRec10          .           .           .          .          .         
## PctRecentImmig         .           .           .          .          .         
## PctRecImmig5           .           .           .          .          .         
## PctRecImmig8           .           .           .          .          .         
## PctRecImmig10          .           .           .          .          .         
## PctSpeakEnglOnly       .           .           .          .          .         
## PctNotSpeakEnglWell    .           .           .          .          .         
## PctLargHouseFam        .           .           .          .          .         
## PctLargHouseOccup      .           .           .          .          .         
## PersPerOccupHous       .           .           .          .          .         
## PersPerOwnOccHous      .           .           .          .          .         
## PersPerRentOccHous     .           .           .          .          .         
## PctPersOwnOccup        .           .           .          .          .         
## PctPersDenseHous       .           .           .          .          .         
## PctHousLess3BR         .           .           .          .          .         
## MedNumBR               .           .           .          .          .         
## HousVacant             .           .           .          .          0.01401508
## PctHousOccup           .           .           .          .          .         
## PctHousOwnOcc          .           .           .          .          .         
## PctVacantBoarded       .           .           .          .          .         
## PctVacMore6Mos         .           .           .          .          .         
## MedYrHousBuilt         .           .           .          .          .         
## PctHousNoPhone         .           .           .          .          .         
## PctWOFullPlumb         .           .           .          .          .         
## OwnOccLowQuart         .           .           .          .          .         
## OwnOccMedVal           .           .           .          .          .         
## OwnOccHiQuart          .           .           .          .          .         
## RentLowQ               .           .           .          .          .         
## RentMedian             .           .           .          .          .         
## RentHighQ              .           .           .          .          .         
## MedRent                .           .           .          .          .         
## MedRentPctHousInc      .           .           .          .          .         
## MedOwnCostPctInc       .           .           .          .          .         
## MedOwnCostPctIncNoMtg  .           .           .          .          .         
## NumInShelters          .           .           .          .          .         
## NumStreet              .           .           .          .          .         
## PctForeignBorn         .           .           .          .          .         
## PctBornSameState       .           .           .          .          .         
## PctSameHouse85         .           .           .          .          .         
## PctSameCity85          .           .           .          .          .         
## PctSameState85         .           .           .          .          .         
## LandArea               .           .           .          .          .         
## PopDens                .           .           .          .          .         
## PctUsePubTrans         .           .           .          .          .         
##                                                                      
## (Intercept)            0.49739984  0.50875827  0.51891852  0.51804327
## (Intercept)            .           .           .           .         
## state                  .           .           .           .         
## fold                   .           .           .           .         
## population             .           .           .           .         
## householdsize          .           .           .           .         
## racepctblack           .           .           .           .         
## racePctWhite          -0.13935519 -0.14887589 -0.15752139 -0.16521742
## racePctAsian           .           .           .           .         
## racePctHisp            .           .           .           .         
## agePct12t21            .           .           .           .         
## agePct12t29            .           .           .           .         
## agePct16t24            .           .           .           .         
## agePct65up             .           .           .           .         
## numbUrban              .           .           .           .         
## pctUrban               .           .           .           .         
## medIncome              .           .           .           .         
## pctWWage               .           .           .           .         
## pctWFarmSelf           .           .           .           .         
## pctWInvInc             .           .           .           .         
## pctWSocSec             .           .           .           .         
## pctWPubAsst            .           .           .           .         
## pctWRetire             .           .           .           .         
## medFamInc              .           .           .           .         
## perCapInc              .           .           .           .         
## whitePerCap            .           .           .           .         
## blackPerCap            .           .           .           .         
## indianPerCap           .           .           .           .         
## AsianPerCap            .           .           .           .         
## OtherPerCap            .           .           .           .         
## HispPerCap             .           .           .           .         
## NumUnderPov            .           .           .           .         
## PctPopUnderPov         .           .           .           .         
## PctLess9thGrade        .           .           .           .         
## PctNotHSGrad           .           .           .           .         
## PctBSorMore            .           .           .           .         
## PctUnemployed          .           .           .           .         
## PctEmploy              .           .           .           .         
## PctEmplManu            .           .           .           .         
## PctEmplProfServ        .           .           .           .         
## PctOccupManu           .           .           .           .         
## PctOccupMgmtProf       .           .           .           .         
## MalePctDivorce         .           .           .           .         
## MalePctNevMarr         .           .           .           .         
## FemalePctDiv           .           .           .           .         
## TotalPctDiv            .           .           .           0.00812099
## PersPerFam             .           .           .           .         
## PctFam2Par             .           .           .           .         
## PctKids2Par           -0.32042909 -0.32906677 -0.33674023 -0.33539208
## PctYoungKids2Par       .           .           .           .         
## PctTeen2Par            .           .           .           .         
## PctWorkMomYoungKids    .           .           .           .         
## PctWorkMom             .           .           .           .         
## NumIlleg               .           .           .           .         
## PctIlleg               0.16693519  0.16552037  0.16440674  0.16694965
## NumImmig               .           .           .           .         
## PctImmigRecent         .           .           .           .         
## PctImmigRec5           .           .           .           .         
## PctImmigRec8           .           .           .           .         
## PctImmigRec10          .           .           .           .         
## PctRecentImmig         .           .           .           .         
## PctRecImmig5           .           .           .           .         
## PctRecImmig8           .           .           .           .         
## PctRecImmig10          .           .           .           .         
## PctSpeakEnglOnly       .           .           .           .         
## PctNotSpeakEnglWell    .           .           .           .         
## PctLargHouseFam        .           .           .           .         
## PctLargHouseOccup      .           .           .           .         
## PersPerOccupHous       .           .           .           .         
## PersPerOwnOccHous      .           .           .           .         
## PersPerRentOccHous     .           .           .           .         
## PctPersOwnOccup        .           .           .           .         
## PctPersDenseHous       .           .           .           .         
## PctHousLess3BR         .           .           .           .         
## MedNumBR               .           .           .           .         
## HousVacant             0.03589051  0.05583704  0.07402335  0.08950784
## PctHousOccup           .           .           .           .         
## PctHousOwnOcc          .           .           .           .         
## PctVacantBoarded       .           .           .           .         
## PctVacMore6Mos         .           .           .           .         
## MedYrHousBuilt         .           .           .           .         
## PctHousNoPhone         .           .           .           .         
## PctWOFullPlumb         .           .           .           .         
## OwnOccLowQuart         .           .           .           .         
## OwnOccMedVal           .           .           .           .         
## OwnOccHiQuart          .           .           .           .         
## RentLowQ               .           .           .           .         
## RentMedian             .           .           .           .         
## RentHighQ              .           .           .           .         
## MedRent                .           .           .           .         
## MedRentPctHousInc      .           .           .           .         
## MedOwnCostPctInc       .           .           .           .         
## MedOwnCostPctIncNoMtg  .           .           .           .         
## NumInShelters          .           .           .           .         
## NumStreet              .           .           .           .         
## PctForeignBorn         .           .           .           .         
## PctBornSameState       .           .           .           .         
## PctSameHouse85         .           .           .           .         
## PctSameCity85          .           .           .           .         
## PctSameState85         .           .           .           .         
## LandArea               .           .           .           .         
## PopDens                .           .           .           .         
## PctUsePubTrans         .           .           .           .         
##                                                                        
## (Intercept)            0.510239819  0.501044507  0.49099645  0.48204741
## (Intercept)            .            .            .           .         
## state                  .            .            .           .         
## fold                   .            .            .           .         
## population             .            .            .           .         
## householdsize          .            .            .           .         
## racepctblack           .            .            .           .         
## racePctWhite          -0.171592314 -0.172038063 -0.17308383 -0.17447569
## racePctAsian           .            .            .           .         
## racePctHisp            .            .            .           .         
## agePct12t21            .            .            .           .         
## agePct12t29            .            .            .           .         
## agePct16t24            .            .            .           .         
## agePct65up             .            .            .           .         
## numbUrban              .            .            .           .         
## pctUrban               .            .            .           .         
## medIncome              .            .            .           .         
## pctWWage               .            .            .           .         
## pctWFarmSelf           .            .            .           .         
## pctWInvInc             .            .            .           .         
## pctWSocSec             .            .            .           .         
## pctWPubAsst            .            .            .           .         
## pctWRetire             .            .            .           .         
## medFamInc              .            .            .           .         
## perCapInc              .            .            .           .         
## whitePerCap            .            .            .           .         
## blackPerCap            .            .            .           .         
## indianPerCap           .            .            .           .         
## AsianPerCap            .            .            .           .         
## OtherPerCap            .            .            .           .         
## HispPerCap             .            .            .           .         
## NumUnderPov            .            .            .           .         
## PctPopUnderPov         .            .            .           .         
## PctLess9thGrade        .            .            .           .         
## PctNotHSGrad           .            .            .           .         
## PctBSorMore            .            .            .           .         
## PctUnemployed          .            .            .           .         
## PctEmploy              .            .            .           .         
## PctEmplManu            .            .            .           .         
## PctEmplProfServ        .            .            .           .         
## PctOccupManu           .            .            .           .         
## PctOccupMgmtProf       .            .            .           .         
## MalePctDivorce         .            0.008170492  0.02350898  0.04257078
## MalePctNevMarr         .            .            .           .         
## FemalePctDiv           .            .            .           .         
## TotalPctDiv            0.020577876  0.021528748  0.01599127  0.00613436
## PersPerFam             .            .            .           .         
## PctFam2Par             .            .            .           .         
## PctKids2Par           -0.328691333 -0.325878436 -0.32097957 -0.31622984
## PctYoungKids2Par       .            .            .           .         
## PctTeen2Par            .            .            .           .         
## PctWorkMomYoungKids    .            .            .           .         
## PctWorkMom             .            .            .           .         
## NumIlleg               .            .            .           .         
## PctIlleg               0.171227041  0.175334590  0.17988992  0.18391337
## NumImmig               .            .            .           .         
## PctImmigRecent         .            .            .           .         
## PctImmigRec5           .            .            .           .         
## PctImmigRec8           .            .            .           .         
## PctImmigRec10          .            .            .           .         
## PctRecentImmig         .            .            .           .         
## PctRecImmig5           .            .            .           .         
## PctRecImmig8           .            .            .           .         
## PctRecImmig10          .            .            .           .         
## PctSpeakEnglOnly       .            .            .           .         
## PctNotSpeakEnglWell    .            .            .           .         
## PctLargHouseFam        .            .            .           .         
## PctLargHouseOccup      .            .            .           .         
## PersPerOccupHous       .            .            .           .         
## PersPerOwnOccHous      .            .            .           .         
## PersPerRentOccHous     .            .            .           .         
## PctPersOwnOccup        .            .            .           .         
## PctPersDenseHous       0.001410899  0.010304964  0.01863110  0.02632764
## PctHousLess3BR         .            .            .           .         
## MedNumBR               .            .            .           .         
## HousVacant             0.098594259  0.105988600  0.11253576  0.11838301
## PctHousOccup           .            .            .           .         
## PctHousOwnOcc          .            .            .           .         
## PctVacantBoarded       .            .            .           .         
## PctVacMore6Mos         .            .            .           .         
## MedYrHousBuilt         .            .            .           .         
## PctHousNoPhone         .            .            .           .         
## PctWOFullPlumb         .            .            .           .         
## OwnOccLowQuart         .            .            .           .         
## OwnOccMedVal           .            .            .           .         
## OwnOccHiQuart          .            .            .           .         
## RentLowQ               .            .            .           .         
## RentMedian             .            .            .           .         
## RentHighQ              .            .            .           .         
## MedRent                .            .            .           .         
## MedRentPctHousInc      .            .            .           .         
## MedOwnCostPctInc       .            .            .           .         
## MedOwnCostPctIncNoMtg  .            .            .           .         
## NumInShelters          .            .            .           .         
## NumStreet              0.011512470  0.024478737  0.03613088  0.04672960
## PctForeignBorn         .            .            .           .         
## PctBornSameState       .            .            .           .         
## PctSameHouse85         .            .            .           .         
## PctSameCity85          .            .            .           .         
## PctSameState85         .            .            .           .         
## LandArea               .            .            .           .         
## PopDens                .            .            .           .         
## PctUsePubTrans         .            .            .           .         
##                                                                              
## (Intercept)            4.754156e-01  0.4696311002  0.4629715678  0.4589318859
## (Intercept)            .             .             .             .           
## state                 -6.536632e-05 -0.0001525771 -0.0002430974 -0.0003211076
## fold                   .             .             .             .           
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           .             .             .             .           
## racePctWhite          -1.749973e-01 -0.1740921963 -0.1734769667 -0.1734975054
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29            .             .             .             .           
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               .             .             .             0.0014936499
## medIncome              .             .             .             .           
## pctWWage               .             .             .             .           
## pctWFarmSelf           .             .             .             .           
## pctWInvInc             .             .             .             .           
## pctWSocSec             .             .             .             .           
## pctWPubAsst            .             .             .             .           
## pctWRetire             .             .             .             .           
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .             .           
## blackPerCap            .             .             .             .           
## indianPerCap           .             .             .             .           
## AsianPerCap            .             .             .             .           
## OtherPerCap            .             .             .             .           
## HispPerCap             .             .             .             .           
## NumUnderPov            .             .             .             .           
## PctPopUnderPov         .             .             .             .           
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .             .             .           
## PctEmploy              .             .             .             .           
## PctEmplManu            .             .             .             .           
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         5.716011e-02  0.0637998700  0.0704139980  0.0773060273
## MalePctNevMarr         .             .             .             .           
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -3.121808e-01 -0.3091819160 -0.3046471316 -0.3016743792
## PctYoungKids2Par       .             .             .             .           
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom             .             .             .            -0.0033347329
## NumIlleg               .             .             .             .           
## PctIlleg               1.878857e-01  0.1919477666  0.1943782947  0.1957624355
## NumImmig               .             .             .             .           
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .             .             .           
## PctPersDenseHous       3.215887e-02  0.0371031389  0.0412731769  0.0436930378
## PctHousLess3BR         .             .             .             .           
## MedNumBR               .             .             .             .           
## HousVacant             1.236602e-01  0.1286673812  0.1316366764  0.1331850227
## PctHousOccup           .             .             .             .           
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       .             0.0008907537  0.0064892650  0.0109728973
## PctVacMore6Mos         .             .             .             .           
## MedYrHousBuilt         .             .             .             .           
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ               .             .             .             .           
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             .             .             .           
## MedRentPctHousInc      .             .             .             .           
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg  .             .             .             .           
## NumInShelters          .             .             .             .           
## NumStreet              5.573299e-02  0.0634915917  0.0709332465  0.0776267539
## PctForeignBorn         .             .             .             .           
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          .             .             .             .           
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                              
## (Intercept)            0.4596702494  0.4615500800  0.4509845545  0.4377146475
## (Intercept)            .             .             .             .           
## state                 -0.0003878423 -0.0004488919 -0.0005081911 -0.0005654171
## fold                   .             .             .             .           
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           .             .             0.0100682689  0.0243910208
## racePctWhite          -0.1728148879 -0.1724886286 -0.1635782672 -0.1507048697
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29            .             .             .             .           
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0045620679  0.0076901986  0.0104401482  0.0129913262
## medIncome              .             .             .             .           
## pctWWage               .             .             .             .           
## pctWFarmSelf           .             .             .             .           
## pctWInvInc             .             .             .             .           
## pctWSocSec             .             .             .             .           
## pctWPubAsst            .             .             .             .           
## pctWRetire             .             .             .             .           
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .             .           
## blackPerCap            .             .             .             .           
## indianPerCap           .             .             .             .           
## AsianPerCap            .             .             .             .           
## OtherPerCap            .             .             .             .           
## HispPerCap             .             .             .             .           
## NumUnderPov            .             .             .             .           
## PctPopUnderPov         .             .             .             .           
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .             .             .           
## PctEmploy              .             .             .             .           
## PctEmplManu            .             .             .             .           
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.0827139379  0.0874402147  0.0933416620  0.0980631911
## MalePctNevMarr         .             .             .             .           
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.3016120668 -0.2989499300 -0.2923723366 -0.2876813375
## PctYoungKids2Par       .             .             .             .           
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0099168915 -0.0154880506 -0.0214561748 -0.0271822017
## NumIlleg               .             .             .             .           
## PctIlleg               0.1966425629  0.1988003896  0.1995621203  0.1976636987
## NumImmig               .             .             .             .           
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .             .             .           
## PctPersDenseHous       0.0448119610  0.0457185911  0.0526862497  0.0623139156
## PctHousLess3BR         .             .             .             .           
## MedNumBR               .             .             .             .           
## HousVacant             0.1322513665  0.1286187625  0.1257597866  0.1236764509
## PctHousOccup          -0.0023697847 -0.0088766577 -0.0141032365 -0.0181840229
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0141398790  0.0171280860  0.0195200084  0.0215327611
## PctVacMore6Mos         .             .             .             .           
## MedYrHousBuilt         .             .             .             .           
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ               .             .             .             .           
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             .             .             .           
## MedRentPctHousInc      .             .             .             .           
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg  .             .             .             .           
## NumInShelters          .             .             .             .           
## NumStreet              0.0846207118  0.0930040727  0.1004293654  0.1071000227
## PctForeignBorn         .             .             .             .           
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          .             .             .             .           
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                             
## (Intercept)            0.4260047083  0.4153567185  0.4056720268  0.397865561
## (Intercept)            .             .             .             .          
## state                 -0.0006174526 -0.0006648481 -0.0007080246 -0.000745687
## fold                   .             .             .             .          
## population             .             .             .             .          
## householdsize          .             .             .             .          
## racepctblack           0.0372240076  0.0488883559  0.0595024744  0.069689214
## racePctWhite          -0.1391248139 -0.1285957327 -0.1190138700 -0.109966304
## racePctAsian           .             .             .             .          
## racePctHisp            .             .             .             .          
## agePct12t21            .             .             .             .          
## agePct12t29            .             .             .            -0.002499253
## agePct16t24            .             .             .             .          
## agePct65up             .             .             .             .          
## numbUrban              .             .             .             .          
## pctUrban               0.0153233652  0.0174481920  0.0193844230  0.021080151
## medIncome              .             .             .             .          
## pctWWage               .             .             .             .          
## pctWFarmSelf           .             .             .             .          
## pctWInvInc             .             .             .             .          
## pctWSocSec             .             .             .             .          
## pctWPubAsst            .             .             .             .          
## pctWRetire             .             .             .             .          
## medFamInc              .             .             .             .          
## perCapInc              .             .             .             .          
## whitePerCap            .             .             .             .          
## blackPerCap            .             .             .             .          
## indianPerCap           .             .             .             .          
## AsianPerCap            .             .             .             .          
## OtherPerCap            .             .             .             .          
## HispPerCap             .             .             .             .          
## NumUnderPov            .             .             .             .          
## PctPopUnderPov         .             .             .             .          
## PctLess9thGrade        .             .             .             .          
## PctNotHSGrad           .             .             .             .          
## PctBSorMore            .             .             .             .          
## PctUnemployed          .             .             .             .          
## PctEmploy              .             .             .             .          
## PctEmplManu            .             .             .             .          
## PctEmplProfServ        .             .             .             .          
## PctOccupManu           .             .             .             .          
## PctOccupMgmtProf       .             .             .             .          
## MalePctDivorce         0.1022432644  0.1060514579  0.1095186392  0.112244451
## MalePctNevMarr         .             .             .             .          
## FemalePctDiv           .             .             .             .          
## TotalPctDiv            .             .             .             .          
## PersPerFam             .             .             .             .          
## PctFam2Par             .             .             .             .          
## PctKids2Par           -0.2836593609 -0.2799931445 -0.2766585104 -0.274115884
## PctYoungKids2Par       .             .             .             .          
## PctTeen2Par            .             .             .             .          
## PctWorkMomYoungKids    .             .             .             .          
## PctWorkMom            -0.0323895647 -0.0371327285 -0.0414539205 -0.045144908
## NumIlleg               .             .             .             .          
## PctIlleg               0.1959132166  0.1943333226  0.1928965320  0.191475055
## NumImmig               .             .             .             .          
## PctImmigRecent         .             .             .             .          
## PctImmigRec5           .             .             .             .          
## PctImmigRec8           .             .             .             .          
## PctImmigRec10          .             .             .             .          
## PctRecentImmig         .             .             .             .          
## PctRecImmig5           .             .             .             .          
## PctRecImmig8           .             .             .             .          
## PctRecImmig10          .             .             .             .          
## PctSpeakEnglOnly       .             .             .             .          
## PctNotSpeakEnglWell    .             .             .             .          
## PctLargHouseFam        .             .             .             .          
## PctLargHouseOccup      .             .             .             .          
## PersPerOccupHous       .             .             .             .          
## PersPerOwnOccHous      .             .             .             .          
## PersPerRentOccHous     .             .             .             .          
## PctPersOwnOccup        .             .             .             .          
## PctPersDenseHous       0.0709582060  0.0788177875  0.0859702885  0.093214680
## PctHousLess3BR         .             .             .             .          
## MedNumBR               .             .             .             .          
## HousVacant             0.1217933171  0.1200759965  0.1185108569  0.117151592
## PctHousOccup          -0.0219023482 -0.0252937025 -0.0283847386 -0.030955822
## PctHousOwnOcc          .             .             .             .          
## PctVacantBoarded       0.0233578630  0.0250213025  0.0265368992  0.027783033
## PctVacMore6Mos         .             .             .             .          
## MedYrHousBuilt         .             .             .             .          
## PctHousNoPhone         .             .             .             .          
## PctWOFullPlumb         .             .             .             .          
## OwnOccLowQuart         .             .             .             .          
## OwnOccMedVal           .             .             .             .          
## OwnOccHiQuart          .             .             .             .          
## RentLowQ               .             .             .             .          
## RentMedian             .             .             .             .          
## RentHighQ              .             .             .             .          
## MedRent                .             .             .             .          
## MedRentPctHousInc      .             .             .             .          
## MedOwnCostPctInc       .             .             .             .          
## MedOwnCostPctIncNoMtg  .             .             .             .          
## NumInShelters          .             .             .             .          
## NumStreet              0.1131701930  0.1187007765  0.1237400166  0.128369221
## PctForeignBorn         .             .             .             .          
## PctBornSameState       .             .             .             .          
## PctSameHouse85         .             .             .             .          
## PctSameCity85          .             .             .             .          
## PctSameState85         .             .             .             .          
## LandArea               .             .             .             .          
## PopDens                .             .             .             .          
## PctUsePubTrans         .             .             .             .          
##                                                                              
## (Intercept)            0.3937603438  0.3892448120  0.3848451767  3.841359e-01
## (Intercept)            .             .             .             .           
## state                 -0.0007742923 -0.0008015744 -0.0008253778 -8.440405e-04
## fold                   .             .             .            -3.840982e-05
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.0770643529  0.0844682310  0.0914823357  9.710429e-02
## racePctWhite          -0.1020630565 -0.0947515234 -0.0878749075 -8.158592e-02
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29           -0.0084890794 -0.0136092764 -0.0181039082 -2.303420e-02
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0225225293  0.0238374200  0.0249836454  2.612015e-02
## medIncome              .             .             .             .           
## pctWWage               .             .             .             .           
## pctWFarmSelf           .             .             .             .           
## pctWInvInc             .             .            -0.0002946125 -4.032793e-03
## pctWSocSec             .             .             .             .           
## pctWPubAsst            .             .             .             .           
## pctWRetire             .             .             .             .           
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .             .           
## blackPerCap            .             .             .             .           
## indianPerCap           .             .             .             .           
## AsianPerCap            .             .             .             .           
## OtherPerCap            .             .             0.0011755934  4.692997e-03
## HispPerCap             .             .             .             .           
## NumUnderPov            .             .             .             .           
## PctPopUnderPov         .             .             .             .           
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .             .             .           
## PctEmploy              .             .             .             .           
## PctEmplManu            .             .             .             .           
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.1131256359  0.1144353002  0.1155997938  1.150819e-01
## MalePctNevMarr         .             .             .             .           
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2732641460 -0.2718880319 -0.2708850424 -2.708422e-01
## PctYoungKids2Par       .             .             .             .           
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0476426088 -0.0501101045 -0.0523668615 -5.425769e-02
## NumIlleg               .             .             .             .           
## PctIlleg               0.1918216728  0.1917729011  0.1914147003  1.911730e-01
## NumImmig               .             .             .             .           
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .             .             .           
## PctPersDenseHous       0.1000936845  0.1064542015  0.1123847746  1.165768e-01
## PctHousLess3BR         .             .             .             .           
## MedNumBR               .             .             .             .           
## HousVacant             0.1165239229  0.1157394167  0.1150078095  1.146044e-01
## PctHousOccup          -0.0332153453 -0.0352552556 -0.0370761812 -3.864714e-02
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0287459640  0.0296479928  0.0305039711  3.115474e-02
## PctVacMore6Mos         .             .             .             .           
## MedYrHousBuilt         .             .             .             .           
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ               .             .             .             .           
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             .             .             .           
## MedRentPctHousInc      .             .             .             .           
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg  .             .             .            -8.940019e-04
## NumInShelters          .             .             .             .           
## NumStreet              0.1322147602  0.1358571366  0.1392740611  1.427040e-01
## PctForeignBorn         .             .             .             .           
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          .             .             .             .           
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                              
## (Intercept)            0.3874494468  0.3894502297  0.3915517926  0.3912942615
## (Intercept)            .             .             .             .           
## state                 -0.0008543246 -0.0008643687 -0.0008734465 -0.0008785740
## fold                  -0.0002115999 -0.0003703024 -0.0005147981 -0.0006425091
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.1016018155  0.1062287303  0.1103236232  0.1158416045
## racePctWhite          -0.0759341724 -0.0704369548 -0.0656022489 -0.0591770834
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29           -0.0285540516 -0.0334457770 -0.0378933315 -0.0419061639
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0272413372  0.0282451908  0.0291642517  0.0298824294
## medIncome              .             .             .             .           
## pctWWage               .             .             .             .           
## pctWFarmSelf           .             .             .             .           
## pctWInvInc            -0.0082142253 -0.0120099077 -0.0154882015 -0.0194983984
## pctWSocSec             .             .             .             .           
## pctWPubAsst            .             .             .             .           
## pctWRetire             .             .             .             .           
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .             .           
## blackPerCap            .             .             .             .           
## indianPerCap           .             .             .            -0.0022254808
## AsianPerCap            .             .             .             0.0011894979
## OtherPerCap            0.0082376009  0.0114041913  0.0142926970  0.0169153701
## HispPerCap             .             .             .             .           
## NumUnderPov            .             .             .             .           
## PctPopUnderPov         .             .             .             .           
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .             .             .           
## PctEmploy              .             .             .             .           
## PctEmplManu            .             .             .             .           
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.1129855981  0.1114689756  0.1100549129  0.1090247633
## MalePctNevMarr         .             .             .             .           
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2709287623 -0.2703956945 -0.2700243619 -0.2691360260
## PctYoungKids2Par       .             .             .             .           
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0559067123 -0.0574481739 -0.0588531351 -0.0598529531
## NumIlleg               .             .             .             .           
## PctIlleg               0.1928108358  0.1942194575  0.1954208876  0.1963246484
## NumImmig               .             .             .             .           
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          .             .             .             0.0012739858
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .             .             .           
## PctPersDenseHous       0.1192969037  0.1220825372  0.1245044591  0.1260802010
## PctHousLess3BR         .             .             .             .           
## MedNumBR               .             .             .             .           
## HousVacant             0.1138425611  0.1131553555  0.1125145802  0.1119010410
## PctHousOccup          -0.0408503813 -0.0428188513 -0.0446118164 -0.0463137273
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0319812727  0.0327686480  0.0334834422  0.0344721575
## PctVacMore6Mos         .             .             .             .           
## MedYrHousBuilt         .             .             .             .           
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ               .             .             .             .           
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             .             .             .           
## MedRentPctHousInc      .             .             .             .           
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg -0.0052234489 -0.0090983184 -0.0126243445 -0.0162299953
## NumInShelters          .             .             .             0.0003900858
## NumStreet              0.1458114403  0.1486371228  0.1512304736  0.1529975910
## PctForeignBorn         .             .             .             0.0010980459
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          .             .             .             .           
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                              
## (Intercept)            0.3961400927  0.3986661505  0.3997238013  0.4004378862
## (Intercept)            .             .             .             .           
## state                 -0.0008831837 -0.0008834142 -0.0008797144 -0.0008769908
## fold                  -0.0007549116 -0.0008552611 -0.0009425527 -0.0010209732
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.1214760734  0.1270882870  0.1326276477  0.1380748712
## racePctWhite          -0.0525656771 -0.0465907663 -0.0415375938 -0.0369191895
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29           -0.0473329008 -0.0533873335 -0.0575247483 -0.0596406759
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0306706465  0.0314612324  0.0320172198  0.0325829902
## medIncome              .             .             .             .           
## pctWWage               .             .            -0.0009075784 -0.0042867784
## pctWFarmSelf           .             .             .             .           
## pctWInvInc            -0.0240152586 -0.0285226836 -0.0333761850 -0.0384849609
## pctWSocSec             .             .             .             .           
## pctWPubAsst            .             .             .             .           
## pctWRetire            -0.0037054470 -0.0076308972 -0.0116021257 -0.0156932335
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .             .           
## blackPerCap            .             .             .            -0.0009288090
## indianPerCap          -0.0050910483 -0.0076571260 -0.0099148879 -0.0118245790
## AsianPerCap            0.0028807884  0.0045372119  0.0060594877  0.0075422457
## OtherPerCap            0.0190772332  0.0211033895  0.0229914150  0.0248894775
## HispPerCap             .             .             .             .           
## NumUnderPov            .             .             .             .           
## PctPopUnderPov         .             .             .             .           
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .             .             .           
## PctEmploy              .             .             .             .           
## PctEmplManu            .            -0.0004257463 -0.0023235791 -0.0041307573
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.1067935143  0.1042923230  0.1027130999  0.1022434849
## MalePctNevMarr         .             .             .             .           
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2705885772 -0.2693398403 -0.2651591427 -0.2576406787
## PctYoungKids2Par      -0.0003638407 -0.0018550505 -0.0040535630 -0.0062982279
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0608695050 -0.0616552913 -0.0622325572 -0.0625119787
## NumIlleg               .             .             .             .           
## PctIlleg               0.1954306012  0.1946448047  0.1940302002  0.1944308275
## NumImmig               .             .             .             .           
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          0.0024871944  0.0029418106  0.0038031929  0.0050852521
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .             .             .           
## PctPersDenseHous       0.1256312907  0.1252266505  0.1238240721  0.1219164533
## PctHousLess3BR         0.0009737098  0.0035537827  0.0058114689  0.0065789313
## MedNumBR               .             .             .             .           
## HousVacant             0.1097346944  0.1079442220  0.1060347401  0.1036983626
## PctHousOccup          -0.0480532652 -0.0490708951 -0.0502720183 -0.0519829093
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0356030168  0.0367091172  0.0376719594  0.0389765518
## PctVacMore6Mos         .             .            -0.0014574757 -0.0045302148
## MedYrHousBuilt         .             .             .             .           
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ               .             .             .             .           
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             .             .             .           
## MedRentPctHousInc      .             0.0020686785  0.0044570077  0.0063463058
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg -0.0201643834 -0.0241510188 -0.0279232743 -0.0310797529
## NumInShelters          0.0041389476  0.0073142823  0.0104626599  0.0138008176
## NumStreet              0.1532153761  0.1534333142  0.1536021534  0.1537816017
## PctForeignBorn         0.0031785920  0.0049217767  0.0064521863  0.0081028603
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          .             0.0001835284  0.0027001209  0.0055110882
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                             
## (Intercept)            0.4047532227  0.407172533  0.4075711950  0.4218281767
## (Intercept)            .             .            .             .           
## state                 -0.0008757008 -0.000874072 -0.0008741952 -0.0008662521
## fold                  -0.0010950481 -0.001157936 -0.0012117857 -0.0012822283
## population             .             .            .             .           
## householdsize          .             .            .             .           
## racepctblack           0.1408005831  0.143914385  0.1476381735  0.1508715707
## racePctWhite          -0.0351522997 -0.032595554 -0.0291477738 -0.0291762171
## racePctAsian           .             .            .             .           
## racePctHisp            .             .            .             .           
## agePct12t21            .             .            .             .           
## agePct12t29           -0.0624459147 -0.065052446 -0.0673737060 -0.0625795593
## agePct16t24            .             .            .             .           
## agePct65up             .             .            .             .           
## numbUrban              .             .            .             .           
## pctUrban               0.0330748274  0.033509722  0.0339033850  0.0340245317
## medIncome              .             .            .             .           
## pctWWage              -0.0060493725 -0.007818111 -0.0089487074 -0.0136695905
## pctWFarmSelf           .             .            .             0.0016874962
## pctWInvInc            -0.0415765187 -0.045438522 -0.0485619704 -0.0492392161
## pctWSocSec             .             .            .             .           
## pctWPubAsst            .             .            .             .           
## pctWRetire            -0.0195227630 -0.022835245 -0.0256436734 -0.0315264013
## medFamInc              .             .            .             .           
## perCapInc              .             .            .             .           
## whitePerCap            .             .            .             .           
## blackPerCap           -0.0034176453 -0.005559969 -0.0072136876 -0.0080872559
## indianPerCap          -0.0135197792 -0.015030804 -0.0162930516 -0.0173944371
## AsianPerCap            0.0089093100  0.010200162  0.0115220077  0.0132242455
## OtherPerCap            0.0267289406  0.028378700  0.0300176057  0.0318417655
## HispPerCap             .             .            .             .           
## NumUnderPov            .             .            .             .           
## PctPopUnderPov         .             .           -0.0010177734 -0.0189730354
## PctLess9thGrade        .             .            .             .           
## PctNotHSGrad           .             .            .             .           
## PctBSorMore            .             .            .             .           
## PctUnemployed          .             .            .             .           
## PctEmploy              .             .            .             .           
## PctEmplManu           -0.0056198933 -0.006890380 -0.0077965049 -0.0090066428
## PctEmplProfServ        .             .            .             .           
## PctOccupManu           .             .            .             .           
## PctOccupMgmtProf       .             .            .             .           
## MalePctDivorce         0.1012995184  0.100311452  0.0995635751  0.0984749610
## MalePctNevMarr         .             .            .             .           
## FemalePctDiv           .             .            .             .           
## TotalPctDiv            .             .            .             .           
## PersPerFam             .             .            .             .           
## PctFam2Par             .             .            .             .           
## PctKids2Par           -0.2539315510 -0.249449097 -0.2449651756 -0.2450223677
## PctYoungKids2Par      -0.0075134642 -0.008734988 -0.0096822995 -0.0134127099
## PctTeen2Par            .             .            .             .           
## PctWorkMomYoungKids    .             .            .             .           
## PctWorkMom            -0.0631500571 -0.063611760 -0.0641036291 -0.0681172391
## NumIlleg               .             .            .             .           
## PctIlleg               0.1942835431  0.194575498  0.1949710864  0.1929241562
## NumImmig               .            -0.007491509 -0.0232866758 -0.0352863124
## PctImmigRecent         .             .            .             .           
## PctImmigRec5           .             .            .             .           
## PctImmigRec8           .             .            .             .           
## PctImmigRec10          .             .            .             .           
## PctRecentImmig         .             .            .             .           
## PctRecImmig5           .             .            .             .           
## PctRecImmig8           .             .            .             .           
## PctRecImmig10          0.0057853420  0.007424518  0.0098063491  0.0105094134
## PctSpeakEnglOnly       .             .            .             .           
## PctNotSpeakEnglWell    .             .            .             .           
## PctLargHouseFam        .             .            .             .           
## PctLargHouseOccup      .             .            .             .           
## PersPerOccupHous       .             .            .             .           
## PersPerOwnOccHous      .             .            .             .           
## PersPerRentOccHous     .             .            .             .           
## PctPersOwnOccup        .             .            .             .           
## PctPersDenseHous       0.1194291803  0.117420130  0.1166056010  0.1170447274
## PctHousLess3BR         0.0075331266  0.008337614  0.0089353102  0.0096027207
## MedNumBR               .             .            .             .           
## HousVacant             0.1019038807  0.101339794  0.1013805555  0.1008920107
## PctHousOccup          -0.0534918456 -0.054602115 -0.0556779915 -0.0574440347
## PctHousOwnOcc          .             .            .             .           
## PctVacantBoarded       0.0400986011  0.040891373  0.0413569854  0.0424092439
## PctVacMore6Mos        -0.0072996173 -0.009773134 -0.0120794013 -0.0145090647
## MedYrHousBuilt         .             .            .             .           
## PctHousNoPhone         .             .            .             .           
## PctWOFullPlumb         .             .            .             .           
## OwnOccLowQuart         .             .            .             .           
## OwnOccMedVal           .             .            .             .           
## OwnOccHiQuart          .             .            .             .           
## RentLowQ               .             .           -0.0023248054 -0.0120966305
## RentMedian             .             .            .             .           
## RentHighQ              .             .            .             .           
## MedRent                .             .            .             .           
## MedRentPctHousInc      0.0080292614  0.009663808  0.0119148032  0.0184923439
## MedOwnCostPctInc       .             .            .             .           
## MedOwnCostPctIncNoMtg -0.0336326439 -0.036194477 -0.0385886401 -0.0410493846
## NumInShelters          0.0159114774  0.020080124  0.0270833456  0.0323097197
## NumStreet              0.1543273085  0.156209029  0.1597286294  0.1624803521
## PctForeignBorn         0.0092766812  0.010707098  0.0130978842  0.0160631878
## PctBornSameState       .             .            .             .           
## PctSameHouse85         .             .            .             .           
## PctSameCity85          0.0080203763  0.010388411  0.0125155250  0.0147902042
## PctSameState85         .             .            .             .           
## LandArea               .             .            .             .           
## PopDens                .             .            .             .           
## PctUsePubTrans         .             .            .             .           
##                                                                              
## (Intercept)            0.4351122191  0.4507371230  0.4632772175  0.4741976445
## (Intercept)            .             .             .             .           
## state                 -0.0008581465 -0.0008431946 -0.0008238874 -0.0008132286
## fold                  -0.0013467993 -0.0014023960 -0.0014455411 -0.0014808959
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.1535818419  0.1574108728  0.1616398235  0.1661511465
## racePctWhite          -0.0283557572 -0.0261968304 -0.0240692730 -0.0238839022
## racePctAsian           .             .             .             .           
## racePctHisp            .             .             .             .           
## agePct12t21            .             .             .             .           
## agePct12t29           -0.0584637686 -0.0592190999 -0.0659472046 -0.0744572376
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0342229992  0.0344193488  0.0346205294  0.0348671229
## medIncome              .             .             .             .           
## pctWWage              -0.0180085548 -0.0235810714 -0.0274363063 -0.0349029983
## pctWFarmSelf           0.0040071403  0.0063605923  0.0082580724  0.0102051818
## pctWInvInc            -0.0502278037 -0.0553541465 -0.0642423210 -0.0677263332
## pctWSocSec             .             0.0001278185  0.0006873146  0.0027873172
## pctWPubAsst            .             .             .             .           
## pctWRetire            -0.0366446454 -0.0422559467 -0.0456305609 -0.0489152606
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap            .             .             .            -0.0095647913
## blackPerCap           -0.0089361826 -0.0105542357 -0.0123855792 -0.0137121310
## indianPerCap          -0.0184767235 -0.0197259952 -0.0210134980 -0.0218588737
## AsianPerCap            0.0147215589  0.0154785944  0.0160369013  0.0174281950
## OtherPerCap            0.0334832173  0.0343911859  0.0355494274  0.0365174087
## HispPerCap             0.0002758946  0.0020850317  0.0029810062  0.0047165394
## NumUnderPov            .             .             .             .           
## PctPopUnderPov        -0.0350772930 -0.0505939359 -0.0612280679 -0.0674291370
## PctLess9thGrade        .             .             .             .           
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed          .             .            -0.0037313021 -0.0074964285
## PctEmploy              .             .             0.0002578542  0.0100466156
## PctEmplManu           -0.0100011820 -0.0107625116 -0.0113468581 -0.0124329030
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.0969425547  0.0955556177  0.0953757200  0.0952752071
## MalePctNevMarr         .             0.0054149839  0.0149881802  0.0249238755
## FemalePctDiv           .             .             .             .           
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2476118997 -0.2484510437 -0.2435136299 -0.2364904597
## PctYoungKids2Par      -0.0161995194 -0.0193910550 -0.0238360899 -0.0289451024
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0717707203 -0.0748508293 -0.0776641284 -0.0825220681
## NumIlleg               .             .             .             .           
## PctIlleg               0.1912146984  0.1879897695  0.1837869251  0.1803117490
## NumImmig              -0.0467571385 -0.0563380792 -0.0660013358 -0.0752074022
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .             .             .             .           
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          0.0112162226  0.0105080734  0.0117160840  0.0138962783
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             .             .           
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup        .             .            -0.0007118666 -0.0015972707
## PctPersDenseHous       0.1179146648  0.1208236619  0.1253001799  0.1264237356
## PctHousLess3BR         0.0099351532  0.0094355665  0.0104472229  0.0104540795
## MedNumBR               .             .             .             .           
## HousVacant             0.1010192925  0.1018094582  0.1031758701  0.1042229001
## PctHousOccup          -0.0587099126 -0.0597903385 -0.0605996993 -0.0620501687
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0432324753  0.0437644590  0.0443541489  0.0449886111
## PctVacMore6Mos        -0.0164946752 -0.0186773385 -0.0216572468 -0.0245352448
## MedYrHousBuilt         .            -0.0011976285 -0.0026068424 -0.0035612113
## PctHousNoPhone         .             .             .             .           
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart         .             .             .             .           
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ              -0.0199612428 -0.0291857583 -0.0490639663 -0.0673546198
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                .             0.0042816331  0.0217012451  0.0392089377
## MedRentPctHousInc      0.0239826998  0.0280456440  0.0308890021  0.0332379172
## MedOwnCostPctInc       .             .             .             .           
## MedOwnCostPctIncNoMtg -0.0432324133 -0.0463704012 -0.0497474330 -0.0534039983
## NumInShelters          0.0363958639  0.0393299070  0.0409859355  0.0432301613
## NumStreet              0.1653149320  0.1672610269  0.1692560879  0.1712192849
## PctForeignBorn         0.0184706945  0.0203140369  0.0189788491  0.0188017142
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          0.0166177973  0.0169651111  0.0171611311  0.0174616384
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .             .             .             .           
##                                                                              
## (Intercept)            0.4914211903  5.089766e-01  0.5232527144  0.5402016525
## (Intercept)            .             .             .             .           
## state                 -0.0008161695 -8.132500e-04 -0.0008147154 -0.0008188901
## fold                  -0.0015114943 -1.541044e-03 -0.0015727505 -0.0016000500
## population             .             .             .             .           
## householdsize          .             .             0.0003019397  0.0037445566
## racepctblack           0.1696433892  1.721328e-01  0.1768728478  0.1811957122
## racePctWhite          -0.0271169710 -2.795420e-02 -0.0270828314 -0.0263583723
## racePctAsian           .             .             .             .           
## racePctHisp            0.0002560213  3.096418e-03  0.0049226163  0.0076274518
## agePct12t21            .             .             .             .           
## agePct12t29           -0.0848134709 -9.371704e-02 -0.1057851110 -0.1185058862
## agePct16t24            .             .             .             .           
## agePct65up             .             .             .             .           
## numbUrban              .             .             .             .           
## pctUrban               0.0349165106  3.463083e-02  0.0348870704  0.0350430097
## medIncome              .             .             .             .           
## pctWWage              -0.0417113681 -4.972244e-02 -0.0572570475 -0.0673664277
## pctWFarmSelf           0.0124427964  1.493412e-02  0.0167534041  0.0185337280
## pctWInvInc            -0.0655956098 -6.779015e-02 -0.0714500363 -0.0758966303
## pctWSocSec             0.0058705077  9.223492e-03  0.0114223881  0.0117701621
## pctWPubAsst            .             .             .             .           
## pctWRetire            -0.0537567869 -5.890686e-02 -0.0629083730 -0.0663603725
## medFamInc              .             .             .             .           
## perCapInc              .             .             .             .           
## whitePerCap           -0.0227778012 -3.321007e-02 -0.0408318858 -0.0452441728
## blackPerCap           -0.0149149552 -1.589630e-02 -0.0166660926 -0.0171730563
## indianPerCap          -0.0225581618 -2.333868e-02 -0.0238528168 -0.0242568561
## AsianPerCap            0.0187829757  1.959184e-02  0.0201241297  0.0206629500
## OtherPerCap            0.0372929862  3.765145e-02  0.0382058426  0.0386303262
## HispPerCap             0.0069367126  9.451797e-03  0.0115112574  0.0137184050
## NumUnderPov            .             .             .             .           
## PctPopUnderPov        -0.0746361006 -8.197919e-02 -0.0907521989 -0.0980223205
## PctLess9thGrade       -0.0013437282 -6.518866e-03 -0.0126813612 -0.0182606415
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            .             .             .             .           
## PctUnemployed         -0.0104379268 -1.320054e-02 -0.0147670958 -0.0163476844
## PctEmploy              0.0192451794  2.803823e-02  0.0378849741  0.0485384326
## PctEmplManu           -0.0130681304 -1.306134e-02 -0.0143811641 -0.0156778300
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           .             .             .             .           
## PctOccupMgmtProf       .             .             .             .           
## MalePctDivorce         0.0960615405  9.363046e-02  0.0948629994  0.1007142350
## MalePctNevMarr         0.0361293913  4.474061e-02  0.0562361635  0.0663960179
## FemalePctDiv           .             .            -0.0069053285 -0.0197785203
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2329441326 -2.335516e-01 -0.2305686713 -0.2314596895
## PctYoungKids2Par      -0.0313409057 -3.216130e-02 -0.0343409708 -0.0364173342
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             .             .             .           
## PctWorkMom            -0.0883610877 -9.332040e-02 -0.0980754544 -0.1018771139
## NumIlleg              -0.0015795392 -1.588451e-02 -0.0254909803 -0.0350095997
## PctIlleg               0.1758842876  1.757083e-01  0.1738538915  0.1700096644
## NumImmig              -0.0816396414 -8.568639e-02 -0.0900697054 -0.0935563564
## PctImmigRecent         .             .             .             .           
## PctImmigRec5           .            -2.111165e-05 -0.0007209807 -0.0010468397
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           .             .             .             .           
## PctRecImmig10          0.0134866971  1.105760e-02  0.0116622149  0.0124843152
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell    .             .             .             .           
## PctLargHouseFam        .             .             .             .           
## PctLargHouseOccup      .             .             .             .           
## PersPerOccupHous       .             .             0.0007490883  0.0047537756
## PersPerOwnOccHous      .             .             .             .           
## PersPerRentOccHous     .             .             .             .           
## PctPersOwnOccup       -0.0061307950 -1.002433e-02 -0.0148412984 -0.0213550843
## PctPersDenseHous       0.1272908067  1.280583e-01  0.1283728990  0.1249357590
## PctHousLess3BR         0.0083541698  7.388534e-03  0.0084691223  0.0115066039
## MedNumBR               .             .             .             .           
## HousVacant             0.1052221289  1.109087e-01  0.1150182762  0.1193910178
## PctHousOccup          -0.0638940311 -6.376180e-02 -0.0624243306 -0.0609902113
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0455095912  4.652153e-02  0.0472601950  0.0476869456
## PctVacMore6Mos        -0.0272965626 -2.938216e-02 -0.0317305617 -0.0341478515
## MedYrHousBuilt        -0.0040354649 -3.986581e-03 -0.0049459068 -0.0056359786
## PctHousNoPhone         .             2.338124e-04  0.0056060295  0.0092502076
## PctWOFullPlumb         .             .             .             .           
## OwnOccLowQuart        -0.0042160612 -8.378395e-03 -0.0133789421 -0.0187654962
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ              -0.0863136677 -1.016538e-01 -0.1157127451 -0.1272733009
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                0.0612195519  8.190847e-02  0.1001005191  0.1150791483
## MedRentPctHousInc      0.0353764652  3.766725e-02  0.0405199897  0.0435063909
## MedOwnCostPctInc      -0.0020922511 -7.244814e-03 -0.0109827583 -0.0149636604
## MedOwnCostPctIncNoMtg -0.0563739031 -5.839299e-02 -0.0604972218 -0.0626143356
## NumInShelters          0.0449606945  5.018988e-02  0.0557658837  0.0600979010
## NumStreet              0.1730641117  1.754200e-01  0.1768381214  0.1782382367
## PctForeignBorn         0.0208448061  2.427390e-02  0.0269951588  0.0288697544
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          0.0181943159  1.860673e-02  0.0193528269  0.0202791905
## PctSameState85         .             .             .             .           
## LandArea               .             .             .             .           
## PopDens                .             .             .             .           
## PctUsePubTrans         .            -3.161518e-04 -0.0037546881 -0.0066115234
##                                                                             
## (Intercept)            0.556834824  5.697581e-01  0.5793014436  5.840577e-01
## (Intercept)            .            .             .             .           
## state                 -0.000824504 -8.302962e-04 -0.0008282230 -8.274427e-04
## fold                  -0.001626152 -1.646556e-03 -0.0016690621 -1.686548e-03
## population             .            .             .             .           
## householdsize          0.007423135  1.004444e-02  0.0146675670  1.724499e-02
## racepctblack           0.184323995  1.862461e-01  0.1865809048  1.879479e-01
## racePctWhite          -0.026661327 -2.851002e-02 -0.0308847546 -3.190789e-02
## racePctAsian           .            .             .             .           
## racePctHisp            0.010557151  1.351425e-02  0.0149655630  1.696581e-02
## agePct12t21            0.002143208  1.224598e-02  0.0189418740  2.419845e-02
## agePct12t29           -0.132734217 -1.540542e-01 -0.1717769177 -1.850680e-01
## agePct16t24            .            .             .             .           
## agePct65up             .            .             0.0001149393  5.059345e-03
## numbUrban              .           -1.209772e-03 -0.0053381149 -9.766360e-03
## pctUrban               0.035330227  3.575345e-02  0.0362127717  3.671268e-02
## medIncome              .            .             .             .           
## pctWWage              -0.078198362 -8.632610e-02 -0.0948423945 -1.014485e-01
## pctWFarmSelf           0.019955234  2.143952e-02  0.0232624944  2.480850e-02
## pctWInvInc            -0.080304519 -8.475446e-02 -0.0867773433 -8.911587e-02
## pctWSocSec             0.012132782  1.407931e-02  0.0169372764  1.803322e-02
## pctWPubAsst            .            .             .             .           
## pctWRetire            -0.069540863 -7.184448e-02 -0.0744725359 -7.764970e-02
## medFamInc              .            .             .             .           
## perCapInc              .            .             .             .           
## whitePerCap           -0.048806562 -5.262569e-02 -0.0564218065 -5.966396e-02
## blackPerCap           -0.017748079 -1.843241e-02 -0.0189999077 -1.939436e-02
## indianPerCap          -0.024589690 -2.495286e-02 -0.0252161295 -2.546606e-02
## AsianPerCap            0.021206330  2.177364e-02  0.0224177195  2.296683e-02
## OtherPerCap            0.039096583  3.941744e-02  0.0398153172  4.018888e-02
## HispPerCap             0.015721767  1.759589e-02  0.0193366866  2.088545e-02
## NumUnderPov            .            .             .             .           
## PctPopUnderPov        -0.104136241 -1.095520e-01 -0.1140686441 -1.176338e-01
## PctLess9thGrade       -0.023494958 -2.827185e-02 -0.0345749259 -4.023551e-02
## PctNotHSGrad           .            .             .             .           
## PctBSorMore            .            .             .             1.391122e-04
## PctUnemployed         -0.017231290 -1.750846e-02 -0.0174139184 -1.666526e-02
## PctEmploy              0.060445882  7.310291e-02  0.0858523674  9.682538e-02
## PctEmplManu           -0.016810988 -1.836000e-02 -0.0208021921 -2.255431e-02
## PctEmplProfServ        .            .             .             .           
## PctOccupManu           .            1.346640e-03  0.0046261686  7.108888e-03
## PctOccupMgmtProf       .            .             .             .           
## MalePctDivorce         0.107732136  1.152398e-01  0.1208621311  1.266295e-01
## MalePctNevMarr         0.076171099  8.597559e-02  0.0964565336  1.054700e-01
## FemalePctDiv          -0.033166486 -4.552063e-02 -0.0558934016 -6.506595e-02
## TotalPctDiv            .            .             .             .           
## PersPerFam             .            .             .             .           
## PctFam2Par             .            .             .             .           
## PctKids2Par           -0.233046720 -2.338313e-01 -0.2381526661 -2.408447e-01
## PctYoungKids2Par      -0.037281541 -3.680902e-02 -0.0355963644 -3.427680e-02
## PctTeen2Par            .            .             .             .           
## PctWorkMomYoungKids    .            .             .             .           
## PctWorkMom            -0.105403028 -1.093346e-01 -0.1134650066 -1.168149e-01
## NumIlleg              -0.042982512 -5.009782e-02 -0.0569153353 -6.221127e-02
## PctIlleg               0.166131708  1.635443e-01  0.1618669105  1.605178e-01
## NumImmig              -0.096265986 -9.810651e-02 -0.0988477953 -9.982598e-02
## PctImmigRecent         .            .             .             .           
## PctImmigRec5          -0.001246827 -1.458470e-03 -0.0013254871 -1.443556e-03
## PctImmigRec8           .            .             .             .           
## PctImmigRec10          .            .             .             .           
## PctRecentImmig         .            .             .             .           
## PctRecImmig5           .            .             .             .           
## PctRecImmig8           .            .             .             .           
## PctRecImmig10          0.013055024  1.348307e-02  0.0137750366  1.433103e-02
## PctSpeakEnglOnly       .            .             .             .           
## PctNotSpeakEnglWell    .            .             .            -5.599565e-04
## PctLargHouseFam        .           -4.570750e-03 -0.0177865432 -2.834740e-02
## PctLargHouseOccup      .            .             .            -2.174459e-06
## PersPerOccupHous       0.007931505  1.220729e-02  0.0205266262  3.004547e-02
## PersPerOwnOccHous      .            .             .             .           
## PersPerRentOccHous     .            .             .             .           
## PctPersOwnOccup       -0.028298449 -3.577309e-02 -0.0403722332 -4.528542e-02
## PctPersDenseHous       0.120914416  1.185666e-01  0.1232507896  1.271090e-01
## PctHousLess3BR         0.013948023  1.723298e-02  0.0210607723  2.329566e-02
## MedNumBR               .            .             .             .           
## HousVacant             0.123193526  1.274867e-01  0.1329656500  1.365052e-01
## PctHousOccup          -0.059445167 -5.799251e-02 -0.0568802943 -5.585424e-02
## PctHousOwnOcc          .            .             .             .           
## PctVacantBoarded       0.047879358  4.812779e-02  0.0486928206  4.910958e-02
## PctVacMore6Mos        -0.036335222 -3.859766e-02 -0.0408755721 -4.272128e-02
## MedYrHousBuilt        -0.005995409 -6.629567e-03 -0.0076074892 -8.547952e-03
## PctHousNoPhone         0.012358064  1.483078e-02  0.0174353130  1.928894e-02
## PctWOFullPlumb         .           -4.728094e-05 -0.0013195050 -2.507654e-03
## OwnOccLowQuart        -0.024212998 -2.982638e-02 -0.0344602295 -3.807265e-02
## OwnOccMedVal           .            .             .             .           
## OwnOccHiQuart          .            .             .             .           
## RentLowQ              -0.138870624 -1.490333e-01 -0.1578210266 -1.652819e-01
## RentMedian             .            .             .             .           
## RentHighQ              .            .             .             .           
## MedRent                0.129743904  1.440134e-01  0.1556519818  1.652423e-01
## MedRentPctHousInc      0.046020368  4.775178e-02  0.0486572641  4.950079e-02
## MedOwnCostPctInc      -0.018379815 -2.111580e-02 -0.0233092397 -2.541325e-02
## MedOwnCostPctIncNoMtg -0.064545070 -6.608510e-02 -0.0672323703 -6.844096e-02
## NumInShelters          0.063261146  6.668756e-02  0.0702641393  7.396783e-02
## NumStreet              0.179351038  1.801051e-01  0.1811531022  1.820691e-01
## PctForeignBorn         0.030566744  3.182539e-02  0.0322815470  3.243261e-02
## PctBornSameState       .            .             .             .           
## PctSameHouse85         .            .             .             .           
## PctSameCity85          0.021096491  2.180302e-02  0.0229460091  2.349009e-02
## PctSameState85         .            .             .             .           
## LandArea               .            .             0.0007882646  3.445212e-03
## PopDens                .            .             .             .           
## PctUsePubTrans        -0.009131058 -1.129408e-02 -0.0130463535 -1.451418e-02
##                                                                              
## (Intercept)            5.822872e-01  5.753725e-01  0.5741139446  0.5805356903
## (Intercept)            .             .             .             .           
## state                 -8.199176e-04 -8.151324e-04 -0.0008050362 -0.0007978003
## fold                  -1.698180e-03 -1.701235e-03 -0.0016988577 -0.0017040090
## population             .             .             .             .           
## householdsize          1.933590e-02  1.997993e-02  0.0161030318  0.0143767087
## racepctblack           1.891697e-01  1.924652e-01  0.1956545568  0.1961716942
## racePctWhite          -3.139531e-02 -2.903396e-02 -0.0268935811 -0.0275107056
## racePctAsian           .             .             .             .           
## racePctHisp            2.151145e-02  2.712450e-02  0.0340294438  0.0367404145
## agePct12t21            3.026783e-02  3.300840e-02  0.0348251542  0.0371592813
## agePct12t29           -2.009530e-01 -2.124288e-01 -0.2231776675 -0.2313994717
## agePct16t24            .             .             .             .           
## agePct65up             8.785035e-03  8.874084e-03  0.0090513532  0.0109318801
## numbUrban             -1.591703e-02 -2.094103e-02 -0.0249468244 -0.0291380769
## pctUrban               3.728249e-02  3.785857e-02  0.0386042085  0.0390903401
## medIncome              .             .             .             .           
## pctWWage              -1.127855e-01 -1.220755e-01 -0.1306419257 -0.1371188338
## pctWFarmSelf           2.657062e-02  2.802182e-02  0.0296296595  0.0311307590
## pctWInvInc            -9.591178e-02 -1.018555e-01 -0.1104810303 -0.1165252689
## pctWSocSec             2.157348e-02  2.852832e-02  0.0354295340  0.0370399116
## pctWPubAsst            .             .             .             .           
## pctWRetire            -7.841896e-02 -7.877167e-02 -0.0790373589 -0.0799494917
## medFamInc              .             2.517363e-05  0.0155125082  0.0272513684
## perCapInc              .             .             .             .           
## whitePerCap           -6.670471e-02 -7.315029e-02 -0.0875569067 -0.1011046199
## blackPerCap           -1.997242e-02 -2.026455e-02 -0.0211988724 -0.0222333953
## indianPerCap          -2.581620e-02 -2.608577e-02 -0.0264579622 -0.0269142939
## AsianPerCap            2.352506e-02  2.389378e-02  0.0239540030  0.0239149825
## OtherPerCap            4.078481e-02  4.122199e-02  0.0416787497  0.0420088181
## HispPerCap             2.194357e-02  2.285806e-02  0.0232639676  0.0239533251
## NumUnderPov            .             .             .             .           
## PctPopUnderPov        -1.221637e-01 -1.265689e-01 -0.1296934316 -0.1341452743
## PctLess9thGrade       -4.385866e-02 -4.566432e-02 -0.0468589608 -0.0491885298
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            1.051043e-02  2.126048e-02  0.0317788143  0.0368489742
## PctUnemployed         -1.529848e-02 -1.410240e-02 -0.0128446793 -0.0128270688
## PctEmploy              1.131849e-01  1.259069e-01  0.1355576681  0.1425945807
## PctEmplManu           -2.450373e-02 -2.673841e-02 -0.0295303002 -0.0320433474
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           1.124996e-02  1.564707e-02  0.0210789382  0.0264321844
## PctOccupMgmtProf       .             .             0.0004936109  0.0054724236
## MalePctDivorce         1.365525e-01  1.463284e-01  0.1536557308  0.1586098715
## MalePctNevMarr         1.162798e-01  1.262242e-01  0.1357276339  0.1413228457
## FemalePctDiv          -7.753579e-02 -8.787823e-02 -0.0970980555 -0.1052066860
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -2.462675e-01 -2.474849e-01 -0.2479655163 -0.2534058209
## PctYoungKids2Par      -3.240358e-02 -3.164669e-02 -0.0319482648 -0.0316088959
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    .             3.575735e-04  0.0058657389  0.0101342443
## PctWorkMom            -1.206284e-01 -1.239671e-01 -0.1303043258 -0.1360160931
## NumIlleg              -6.610603e-02 -6.914788e-02 -0.0686144628 -0.0703568958
## PctIlleg               1.586057e-01  1.568128e-01  0.1531367116  0.1504172587
## NumImmig              -9.950434e-02 -9.967879e-02 -0.1007481179 -0.1018474845
## PctImmigRecent         .             .             .             .           
## PctImmigRec5          -1.607990e-03 -2.129246e-03 -0.0018770158 -0.0019589704
## PctImmigRec8           .             .             .             .           
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           2.569853e-05  6.289237e-03  0.0084162808  0.0121482843
## PctRecImmig10          1.643737e-02  1.349215e-02  0.0115865905  0.0073595556
## PctSpeakEnglOnly       .             .             .             .           
## PctNotSpeakEnglWell   -9.311398e-03 -2.071641e-02 -0.0369417957 -0.0433482083
## PctLargHouseFam       -4.348623e-02 -5.622283e-02 -0.0595679488 -0.0603197650
## PctLargHouseOccup     -1.025806e-04 -1.754620e-04 -0.0081959542 -0.0148749284
## PersPerOccupHous       4.716075e-02  6.786149e-02  0.1229028930  0.1580680475
## PersPerOwnOccHous     -3.066892e-05 -2.368124e-03 -0.0279886659 -0.0449488190
## PersPerRentOccHous     .            -2.257811e-03 -0.0183427824 -0.0290009871
## PctPersOwnOccup       -5.028085e-02 -5.618339e-02 -0.0687314684 -0.0764541753
## PctPersDenseHous       1.350166e-01  1.410151e-01  0.1470200300  0.1506684124
## PctHousLess3BR         2.745311e-02  3.097674e-02  0.0360352221  0.0404172699
## MedNumBR               .             8.041263e-04  0.0028870743  0.0045165918
## HousVacant             1.415893e-01  1.454959e-01  0.1474526885  0.1510753802
## PctHousOccup          -5.451897e-02 -5.408864e-02 -0.0543852690 -0.0542893429
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       4.962442e-02  5.015913e-02  0.0512124976  0.0519682656
## PctVacMore6Mos        -4.495348e-02 -4.700972e-02 -0.0490167622 -0.0506453188
## MedYrHousBuilt        -9.021263e-03 -9.121462e-03 -0.0091977074 -0.0095195354
## PctHousNoPhone         1.973440e-02  2.020212e-02  0.0205079140  0.0211912317
## PctWOFullPlumb        -3.647853e-03 -4.437145e-03 -0.0049280738 -0.0055017139
## OwnOccLowQuart        -4.275646e-02 -4.777755e-02 -0.0564222153 -0.0617737715
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             .             .           
## RentLowQ              -1.737906e-01 -1.801525e-01 -0.1853198094 -0.1895356165
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                1.764234e-01  1.853036e-01  0.1938667612  0.2009483112
## MedRentPctHousInc      4.945060e-02  4.951001e-02  0.0481686012  0.0478496060
## MedOwnCostPctInc      -2.747286e-02 -2.929504e-02 -0.0296001304 -0.0305097164
## MedOwnCostPctIncNoMtg -6.908225e-02 -6.997734e-02 -0.0712135601 -0.0717042728
## NumInShelters          7.738241e-02  8.163061e-02  0.0860607188  0.0893355438
## NumStreet              1.825606e-01  1.826236e-01  0.1823450634  0.1827054476
## PctForeignBorn         3.445977e-02  3.861615e-02  0.0488006410  0.0540548604
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          2.489721e-02  2.545015e-02  0.0258300758  0.0260060338
## PctSameState85         .             1.593011e-03  0.0032545997  0.0045087398
## LandArea               5.384246e-03  6.934200e-03  0.0086779347  0.0102068420
## PopDens                .             .             .             .           
## PctUsePubTrans        -1.633618e-02 -1.837944e-02 -0.0208251679 -0.0228053956
##                                                                              
## (Intercept)            5.834766e-01  0.5867939833  0.5891368148  0.5912861860
## (Intercept)            .             .             .             .           
## state                 -7.909477e-04 -0.0007858794 -0.0007806395 -0.0007731370
## fold                  -1.707660e-03 -0.0017100313 -0.0017130955 -0.0017153592
## population             .             .             .             .           
## householdsize          1.250944e-02  0.0109853844  0.0094868329  0.0068544101
## racepctblack           1.972102e-01  0.1978777359  0.1984823282  0.1998046417
## racePctWhite          -2.757767e-02 -0.0279480711 -0.0281623042 -0.0281460015
## racePctAsian           .             .             .             .           
## racePctHisp            3.968366e-02  0.0421725769  0.0445627363  0.0474647494
## agePct12t21            3.887144e-02  0.0407171466  0.0421361388  0.0417246540
## agePct12t29           -2.384784e-01 -0.2451626702 -0.2508080740 -0.2542710696
## agePct16t24            .             .             .             .           
## agePct65up             1.262499e-02  0.0141380334  0.0156289950  0.0165002062
## numbUrban             -3.319577e-02 -0.0368177420 -0.0403354875 -0.0430758236
## pctUrban               3.958776e-02  0.0400617425  0.0404950422  0.0408777877
## medIncome              .             .             .             .           
## pctWWage              -1.435532e-01 -0.1487669827 -0.1536923855 -0.1586741716
## pctWFarmSelf           3.248282e-02  0.0336520363  0.0347583073  0.0357866572
## pctWInvInc            -1.219396e-01 -0.1267180313 -0.1310729653 -0.1353148359
## pctWSocSec             3.927297e-02  0.0414632333  0.0436425404  0.0469705549
## pctWPubAsst            .             .             .             .           
## pctWRetire            -8.072193e-02 -0.0814707917 -0.0821092834 -0.0827025770
## medFamInc              3.962652e-02  0.0502009501  0.0603929017  0.0728161169
## perCapInc              .             .             .             .           
## whitePerCap           -1.136289e-01 -0.1246292766 -0.1348951520 -0.1489006610
## blackPerCap           -2.315912e-02 -0.0239358980 -0.0246614266 -0.0252797754
## indianPerCap          -2.730768e-02 -0.0276577806 -0.0279677724 -0.0283366850
## AsianPerCap            2.386254e-02  0.0238124005  0.0237732537  0.0236448056
## OtherPerCap            4.235588e-02  0.0426492109  0.0429480342  0.0433315843
## HispPerCap             2.446142e-02  0.0249296609  0.0253116705  0.0257099281
## NumUnderPov            .             .             .             .           
## PctPopUnderPov        -1.371964e-01 -0.1403779045 -0.1429794643 -0.1443559667
## PctLess9thGrade       -5.131514e-02 -0.0531177249 -0.0547521333 -0.0565692875
## PctNotHSGrad           .             .             .             .           
## PctBSorMore            4.019921e-02  0.0431119096  0.0455447690  0.0480357397
## PctUnemployed         -1.259370e-02 -0.0123550179 -0.0120583502 -0.0118775190
## PctEmploy              1.499009e-01  0.1562679486  0.1623518567  0.1680803601
## PctEmplManu           -3.451071e-02 -0.0366996818 -0.0387448061 -0.0404816174
## PctEmplProfServ        .             .             .             .           
## PctOccupManu           3.182499e-02  0.0366284486  0.0411758434  0.0447249328
## PctOccupMgmtProf       1.120492e-02  0.0162660525  0.0213073505  0.0257992422
## MalePctDivorce         1.632954e-01  0.1673019144  0.1709900120  0.1747095796
## MalePctNevMarr         1.469871e-01  0.1520612130  0.1566579274  0.1604508139
## FemalePctDiv          -1.124545e-01 -0.1188392063 -0.1247099916 -0.1303890136
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -2.573646e-01 -0.2609602081 -0.2644481597 -0.2685197666
## PctYoungKids2Par      -3.133247e-02 -0.0311486612 -0.0309198395 -0.0307633066
## PctTeen2Par            .             .             .             .           
## PctWorkMomYoungKids    1.407451e-02  0.0175615502  0.0207583814  0.0235983238
## PctWorkMom            -1.411327e-01 -0.1457818573 -0.1500177610 -0.1533281535
## NumIlleg              -7.159431e-02 -0.0727498912 -0.0737810155 -0.0743748761
## PctIlleg               1.478350e-01  0.1454842368  0.1433908239  0.1410586037
## NumImmig              -1.027215e-01 -0.1036392364 -0.1044714295 -0.1058320986
## PctImmigRecent         4.471176e-05  0.0018233948  0.0032946784  0.0046337862
## PctImmigRec5          -2.077574e-03 -0.0036430717 -0.0046479729 -0.0054621124
## PctImmigRec8          -4.357149e-05 -0.0004765115 -0.0011036506 -0.0016690193
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .             .           
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           1.660203e-02  0.0203701026  0.0215598879  0.0229541439
## PctRecImmig10          2.743578e-03  .             .             .           
## PctSpeakEnglOnly       .             .             .            -0.0001200048
## PctNotSpeakEnglWell   -5.036980e-02 -0.0564707285 -0.0624933799 -0.0680036040
## PctLargHouseFam       -6.181636e-02 -0.0632954934 -0.0651266917 -0.0656429932
## PctLargHouseOccup     -2.082054e-02 -0.0257936939 -0.0301993808 -0.0361164868
## PersPerOccupHous       1.920090e-01  0.2207988365  0.2474425169  0.2777697574
## PersPerOwnOccHous     -6.114100e-02 -0.0750911584 -0.0877581372 -0.1011099589
## PersPerRentOccHous    -3.911534e-02 -0.0478297447 -0.0558339737 -0.0641177839
## PctPersOwnOccup       -8.392756e-02 -0.0904661539 -0.0964165521 -0.1022806322
## PctPersDenseHous       1.547051e-01  0.1583895153  0.1621016197  0.1642481970
## PctHousLess3BR         4.448243e-02  0.0478034348  0.0508470630  0.0545556185
## MedNumBR               6.052874e-03  0.0073570029  0.0085751742  0.0098109111
## HousVacant             1.542888e-01  0.1571483999  0.1598662710  0.1624228906
## PctHousOccup          -5.427276e-02 -0.0542942644 -0.0542889372 -0.0541496455
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       5.269332e-02  0.0533726447  0.0540267405  0.0545245506
## PctVacMore6Mos        -5.216306e-02 -0.0535339044 -0.0547946174 -0.0561627483
## MedYrHousBuilt        -9.780775e-03 -0.0099512256 -0.0101086443 -0.0103514737
## PctHousNoPhone         2.167701e-02  0.0223358029  0.0227821792  0.0225744292
## PctWOFullPlumb        -6.026911e-03 -0.0065138077 -0.0069610700 -0.0074524571
## OwnOccLowQuart        -6.730323e-02 -0.0721842596 -0.0766408763 -0.0874580435
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          .             .             0.0001601975  0.0084336425
## RentLowQ              -1.937775e-01 -0.1974997576 -0.2011695717 -0.2041314430
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                2.076489e-01  0.2137257248  0.2192846689  0.2235687206
## MedRentPctHousInc      4.743488e-02  0.0470860508  0.0466987224  0.0458548351
## MedOwnCostPctInc      -3.112988e-02 -0.0316079709 -0.0320122108 -0.0325799255
## MedOwnCostPctIncNoMtg -7.231411e-02 -0.0727961461 -0.0732437555 -0.0736285487
## NumInShelters          9.256008e-02  0.0954364773  0.0981722297  0.1006916919
## NumStreet              1.829500e-01  0.1831881470  0.1833908489  0.1833567778
## PctForeignBorn         5.930631e-02  0.0627223555  0.0658182457  0.0686471608
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          2.609889e-02  0.0261728386  0.0261952813  0.0263025829
## PctSameState85         5.669199e-03  0.0067659166  0.0077252603  0.0086347154
## LandArea               1.156911e-02  0.0128662561  0.0140812152  0.0147787375
## PopDens                .             .            -0.0001285819 -0.0007612172
## PctUsePubTrans        -2.464636e-02 -0.0262528417 -0.0276656631 -0.0289284126
##                                                                              
## (Intercept)            5.967815e-01  0.5996560580  0.6079054883  0.6148287732
## (Intercept)            .             .             .             .           
## state                 -7.708544e-04 -0.0007707348 -0.0007694075 -0.0007672638
## fold                  -1.715289e-03 -0.0017125517 -0.0017128721 -0.0017154207
## population             .             .             .             .           
## householdsize          5.074862e-03  0.0029745336  0.0014339567  .           
## racepctblack           2.012796e-01  0.2025092256  0.2034671816  0.2037642514
## racePctWhite          -2.786279e-02 -0.0285217066 -0.0292414452 -0.0302518379
## racePctAsian          -1.323125e-04 -0.0012641983 -0.0019366805 -0.0026488523
## racePctHisp            4.749734e-02  0.0468193981  0.0470527405  0.0477215590
## agePct12t21            4.224029e-02  0.0442639977  0.0470876802  0.0488435838
## agePct12t29           -2.585841e-01 -0.2639553948 -0.2710285517 -0.2764660587
## agePct16t24            .             .             .             .           
## agePct65up             1.815060e-02  0.0207767904  0.0216778337  0.0206076311
## numbUrban             -4.572852e-02 -0.0478852524 -0.0509818370 -0.0562733751
## pctUrban               4.118981e-02  0.0415182827  0.0417967978  0.0421117822
## medIncome              .             .             .             .           
## pctWWage              -1.615820e-01 -0.1632405864 -0.1676817230 -0.1713322680
## pctWFarmSelf           3.657048e-02  0.0373555687  0.0382686540  0.0391332558
## pctWInvInc            -1.383664e-01 -0.1382527011 -0.1388837694 -0.1400242523
## pctWSocSec             4.916489e-02  0.0481908910  0.0478085246  0.0496663441
## pctWPubAsst            .             .             .             .           
## pctWRetire            -8.322096e-02 -0.0838425371 -0.0843252137 -0.0847167918
## medFamInc              8.249584e-02  0.0871730735  0.0921573888  0.0966800174
## perCapInc              .             .             .             .           
## whitePerCap           -1.607642e-01 -0.1697328514 -0.1793178219 -0.1888923800
## blackPerCap           -2.570340e-02 -0.0260820337 -0.0265858479 -0.0270725951
## indianPerCap          -2.865669e-02 -0.0289081732 -0.0291310998 -0.0293076818
## AsianPerCap            2.363356e-02  0.0235234644  0.0234401745  0.0233770443
## OtherPerCap            4.354743e-02  0.0437287905  0.0439137028  0.0440809960
## HispPerCap             2.619148e-02  0.0265581276  0.0268860012  0.0271235603
## NumUnderPov            .             0.0001855345  0.0107248911  0.0218697940
## PctPopUnderPov        -1.462755e-01 -0.1485240264 -0.1512285571 -0.1540255953
## PctLess9thGrade       -5.872286e-02 -0.0664527066 -0.0738581174 -0.0788175373
## PctNotHSGrad           6.874279e-05  0.0106390672  0.0196200358  0.0251715232
## PctBSorMore            4.998593e-02  0.0512555069  0.0525837604  0.0542904207
## PctUnemployed         -1.174318e-02 -0.0112238614 -0.0104661929 -0.0100317017
## PctEmploy              1.728134e-01  0.1772982251  0.1830817491  0.1874531189
## PctEmplManu           -4.202929e-02 -0.0439635633 -0.0455882119 -0.0474780842
## PctEmplProfServ        .             .            -0.0018447234 -0.0042938983
## PctOccupManu           4.797280e-02  0.0509487903  0.0532769605  0.0561750621
## PctOccupMgmtProf       2.936459e-02  0.0338355340  0.0385443505  0.0441890200
## MalePctDivorce         1.777082e-01  0.1795959924  0.1808688745  0.1822422239
## MalePctNevMarr         1.638927e-01  0.1674348173  0.1712115882  0.1742083635
## FemalePctDiv          -1.344149e-01 -0.1368461997 -0.1404201290 -0.1441837054
## TotalPctDiv            .             .             .             .           
## PersPerFam             .             .             .             .           
## PctFam2Par             .             .             .             .           
## PctKids2Par           -2.714519e-01 -0.2703663468 -0.2705908632 -0.2721383190
## PctYoungKids2Par      -3.054176e-02 -0.0313305990 -0.0315993968 -0.0314462252
## PctTeen2Par            .            -0.0001062522 -0.0012331102 -0.0020507280
## PctWorkMomYoungKids    2.596743e-02  0.0281240276  0.0310334897  0.0333817325
## PctWorkMom            -1.563972e-01 -0.1591830502 -0.1622777874 -0.1647300099
## NumIlleg              -7.483678e-02 -0.0759601581 -0.0824995034 -0.0892009690
## PctIlleg               1.394387e-01  0.1376070597  0.1358162485  0.1343831071
## NumImmig              -1.071073e-01 -0.1075061893 -0.1103631491 -0.1124394789
## PctImmigRecent         5.861491e-03  0.0069294451  0.0077129803  0.0088042375
## PctImmigRec5          -6.196790e-03 -0.0068507747 -0.0070185257 -0.0068660761
## PctImmigRec8          -2.194220e-03 -0.0027845258 -0.0033009262 -0.0040441098
## PctImmigRec10          .             .             .             .           
## PctRecentImmig         .             .             .            -0.0025697130
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           2.531770e-02  0.0273396479  0.0288515895  0.0313489260
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly      -5.508709e-03 -0.0098362401 -0.0133249057 -0.0149516634
## PctNotSpeakEnglWell   -7.246095e-02 -0.0761584377 -0.0808118307 -0.0855625025
## PctLargHouseFam       -6.590946e-02 -0.0656944867 -0.0662232079 -0.0672897468
## PctLargHouseOccup     -3.971961e-02 -0.0414458901 -0.0430869534 -0.0448707026
## PersPerOccupHous       3.008972e-01  0.3177296840  0.3357937368  0.3525525668
## PersPerOwnOccHous     -1.120617e-01 -0.1210860096 -0.1307823103 -0.1392242299
## PersPerRentOccHous    -7.036383e-02 -0.0756756295 -0.0827564055 -0.0890113535
## PctPersOwnOccup       -1.069413e-01 -0.1113270211 -0.1164218002 -0.1211466141
## PctPersDenseHous       1.648976e-01  0.1655977035  0.1680112003  0.1705942066
## PctHousLess3BR         5.783758e-02  0.0592241874  0.0605524005  0.0620449355
## MedNumBR               1.089831e-02  0.0116922038  0.0124778033  0.0132629487
## HousVacant             1.646199e-01  0.1665815602  0.1652556487  0.1651308834
## PctHousOccup          -5.375486e-02 -0.0533039083 -0.0533103077 -0.0531918127
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       5.472918e-02  0.0550193409  0.0552080433  0.0555286175
## PctVacMore6Mos        -5.737982e-02 -0.0584830864 -0.0593974607 -0.0602508107
## MedYrHousBuilt        -1.037071e-02 -0.0104518189 -0.0106736233 -0.0111671910
## PctHousNoPhone         2.292876e-02  0.0234855710  0.0236961656  0.0238456439
## PctWOFullPlumb        -7.795668e-03 -0.0080790107 -0.0085029372 -0.0089151445
## OwnOccLowQuart        -9.880297e-02 -0.1073679609 -0.1177743558 -0.1274699672
## OwnOccMedVal           .             .             .             .           
## OwnOccHiQuart          1.725492e-02  0.0246547286  0.0335498899  0.0417515200
## RentLowQ              -2.067024e-01 -0.2083532427 -0.2106391591 -0.2126831284
## RentMedian             .             .             .             .           
## RentHighQ              .             .             .             .           
## MedRent                2.276497e-01  0.2312664747  0.2361581412  0.2406416503
## MedRentPctHousInc      4.567763e-02  0.0458109688  0.0455596116  0.0453310622
## MedOwnCostPctInc      -3.325024e-02 -0.0340814452 -0.0350502600 -0.0358441302
## MedOwnCostPctIncNoMtg -7.414689e-02 -0.0746405728 -0.0748854842 -0.0751039666
## NumInShelters          1.028897e-01  0.1047290608  0.1059578470  0.1075377671
## NumStreet              1.832944e-01  0.1832715864  0.1836227460  0.1838180126
## PctForeignBorn         6.724566e-02  0.0664379633  0.0665032918  0.0690402500
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          2.612369e-02  0.0253374012  0.0247110677  0.0244073631
## PctSameState85         9.739081e-03  0.0107142545  0.0114749525  0.0121437906
## LandArea               1.559783e-02  0.0159705235  0.0163896973  0.0167169874
## PopDens               -1.018222e-03 -0.0015558916 -0.0021002278 -0.0025982043
## PctUsePubTrans        -3.002181e-02 -0.0311593201 -0.0321514189 -0.0330544859
##                                                                              
## (Intercept)            0.6195961941  0.6234386319  0.6251888072  0.6307192693
## (Intercept)            .             .             .             .           
## state                 -0.0007660077 -0.0007651289 -0.0007664733 -0.0007620868
## fold                  -0.0017176001 -0.0017182672 -0.0017219371 -0.0017169277
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.2041025191  0.2042435728  0.2043900760  0.2047006709
## racePctWhite          -0.0308547433 -0.0314313910 -0.0318054192 -0.0325689794
## racePctAsian          -0.0031030221 -0.0034453046 -0.0038301117 -0.0047050958
## racePctHisp            0.0487094908  0.0495175209  0.0493053180  0.0498528338
## agePct12t21            0.0495482073  0.0496932814  0.0494099702  0.0504857456
## agePct12t29           -0.2808733968 -0.2846111717 -0.2852541973 -0.2903687909
## agePct16t24            .             .             .             .           
## agePct65up             0.0197871321  0.0195371933  0.0201002479  0.0222135441
## numbUrban             -0.0616097861 -0.0664531605 -0.0669318655 -0.0719864486
## pctUrban               0.0424127102  0.0426771720  0.0427211859  0.0429760009
## medIncome              .             .             .             .           
## pctWWage              -0.1738904353 -0.1764295157 -0.1769493636 -0.1803919422
## pctWFarmSelf           0.0398631902  0.0404990151  0.0407509558  0.0415730810
## pctWInvInc            -0.1413809184 -0.1425561106 -0.1427849733 -0.1447443587
## pctWSocSec             0.0518468707  0.0536667634  0.0540028962  0.0536292087
## pctWPubAsst           -0.0005408910 -0.0014623606 -0.0017477321 -0.0019686583
## pctWRetire            -0.0849435767 -0.0850613310 -0.0853904104 -0.0855642622
## medFamInc              0.1014784341  0.1070608696  0.1081787472  0.1138370537
## perCapInc              .             .             .             .           
## whitePerCap           -0.1978500170 -0.2061490329 -0.2079874564 -0.2174806372
## blackPerCap           -0.0274698893 -0.0278010946 -0.0278791571 -0.0284165594
## indianPerCap          -0.0294811313 -0.0296639292 -0.0297559834 -0.0300326796
## AsianPerCap            0.0233187588  0.0232488987  0.0233409275  0.0231529989
## OtherPerCap            0.0442274071  0.0443502652  0.0444419216  0.0445712938
## HispPerCap             0.0273546197  0.0275457332  0.0277389533  0.0278361507
## NumUnderPov            0.0311930777  0.0394127866  0.0415176845  0.0532500318
## PctPopUnderPov        -0.1560487402 -0.1577360933 -0.1586930089 -0.1616565019
## PctLess9thGrade       -0.0815793328 -0.0837755540 -0.0849538723 -0.0901995726
## PctNotHSGrad           0.0279882543  0.0303137118  0.0318085970  0.0376988058
## PctBSorMore            0.0558271945  0.0572737986  0.0583818758  0.0622131259
## PctUnemployed         -0.0094837285 -0.0088074200 -0.0087898481 -0.0079615216
## PctEmploy              0.1914126582  0.1954158442  0.1964646034  0.2019352785
## PctEmplManu           -0.0491093089 -0.0505604819 -0.0509146832 -0.0523636865
## PctEmplProfServ       -0.0062234992 -0.0078560062 -0.0082916784 -0.0100838211
## PctOccupManu           0.0588706390  0.0612181995  0.0614851152  0.0638267464
## PctOccupMgmtProf       0.0488666360  0.0528502753  0.0530895670  0.0565892455
## MalePctDivorce         0.1833964390  0.1844122708  0.1848208564  0.1865357432
## MalePctNevMarr         0.1767402393  0.1794294828  0.1804778486  0.1839669489
## FemalePctDiv          -0.1473311035 -0.1499810090 -0.1508691804 -0.1543414867
## TotalPctDiv            .             .             .             .           
## PersPerFam             .            -0.0001528718 -0.0006394668 -0.0030738307
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2740094479 -0.2758934628 -0.2757785780 -0.2771479363
## PctYoungKids2Par      -0.0314637396 -0.0314960546 -0.0317441521 -0.0319734472
## PctTeen2Par           -0.0026659164 -0.0032434571 -0.0036727495 -0.0042140261
## PctWorkMomYoungKids    0.0352374040  0.0368170480  0.0373854030  0.0392762136
## PctWorkMom            -0.1669720780 -0.1690475464 -0.1700286411 -0.1725766389
## NumIlleg              -0.0947258526 -0.0993586357 -0.1010519657 -0.1081774713
## PctIlleg               0.1331142478  0.1319612424  0.1317127520  0.1303896292
## NumImmig              -0.1142492295 -0.1158619304 -0.1171852394 -0.1190840495
## PctImmigRecent         0.0102740196  0.0117833071  0.0123963287  0.0145623405
## PctImmigRec5          -0.0071044548 -0.0074326042 -0.0076803801 -0.0079579608
## PctImmigRec8          -0.0047049216 -0.0054139169 -0.0058106175 -0.0071555737
## PctImmigRec10          .             .             .             .           
## PctRecentImmig        -0.0063778231 -0.0103142427 -0.0115977161 -0.0186432968
## PctRecImmig5           .             .             .             .           
## PctRecImmig8           0.0347023575  0.0384619706  0.0399274300  0.0474211805
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly      -0.0157713672 -0.0167159381 -0.0172470565 -0.0195192633
## PctNotSpeakEnglWell   -0.0899259878 -0.0940889341 -0.0946635982 -0.0995551707
## PctLargHouseFam       -0.0677101958 -0.0677383592 -0.0686243288 -0.0701635506
## PctLargHouseOccup     -0.0472747149 -0.0498777846 -0.0501016851 -0.0508657095
## PersPerOccupHous       0.3664707269  0.3794467990  0.3825070382  0.3977245606
## PersPerOwnOccHous     -0.1465594652 -0.1530239070 -0.1540609599 -0.1611506107
## PersPerRentOccHous    -0.0942477193 -0.0987491518 -0.0994685427 -0.1047620384
## PctPersOwnOccup       -0.1250967074 -0.1285140629 -0.1293759292 -0.1329131375
## PctPersDenseHous       0.1727379988  0.1747267530  0.1756499428  0.1790476596
## PctHousLess3BR         0.0635490191  0.0651436008  0.0654669759  0.0671945201
## MedNumBR               0.0140102212  0.0147314757  0.0150436891  0.0158459504
## HousVacant             0.1655132038  0.1657451726  0.1654667298  0.1646820383
## PctHousOccup          -0.0530310008 -0.0528570982 -0.0529284895 -0.0527778229
## PctHousOwnOcc          .             .             .             .           
## PctVacantBoarded       0.0558398401  0.0561125153  0.0561759150  0.0565674794
## PctVacMore6Mos        -0.0610537739 -0.0617871477 -0.0621147894 -0.0628111932
## MedYrHousBuilt        -0.0116043130 -0.0119566691 -0.0123062708 -0.0125654713
## PctHousNoPhone         0.0239859947  0.0241078981  0.0244387787  0.0244453377
## PctWOFullPlumb        -0.0092493310 -0.0095367110 -0.0097244028 -0.0100683690
## OwnOccLowQuart        -0.1367909929 -0.1451101406 -0.1467820025 -0.1552260441
## OwnOccMedVal           .             .             0.0002422739  0.0043102967
## OwnOccHiQuart          0.0495828042  0.0568243744  0.0577851235  0.0624268896
## RentLowQ              -0.2145440093 -0.2163946431 -0.2169277229 -0.2189846329
## RentMedian             .             .             .             .           
## RentHighQ             -0.0003110822 -0.0030152120 -0.0037044754 -0.0083485662
## MedRent                0.2447747466  0.2500910378  0.2514091699  0.2580513637
## MedRentPctHousInc      0.0451715803  0.0452327270  0.0455113721  0.0456984965
## MedOwnCostPctInc      -0.0364967979 -0.0369748582 -0.0371783638 -0.0380154868
## MedOwnCostPctIncNoMtg -0.0753465948 -0.0755852523 -0.0759289107 -0.0758422433
## NumInShelters          0.1092813298  0.1109230447  0.1113877005  0.1135167730
## NumStreet              0.1839087381  0.1839603973  0.1843936054  0.1844572422
## PctForeignBorn         0.0720288893  0.0744713635  0.0747305533  0.0760493662
## PctBornSameState       .             .             .             .           
## PctSameHouse85         .             .             .             .           
## PctSameCity85          0.0243576758  0.0242242259  0.0240121111  0.0237572312
## PctSameState85         0.0127285491  0.0132454851  0.0136413922  0.0140726679
## LandArea               0.0171697699  0.0177062112  0.0181201278  0.0185924522
## PopDens               -0.0028745511 -0.0030778597 -0.0033552978 -0.0036165577
## PctUsePubTrans        -0.0338375417 -0.0345258918 -0.0346947009 -0.0354737568
##                                                                              
## (Intercept)            6.337369e-01  6.375051e-01  0.6412110752  6.433449e-01
## (Intercept)            .             .             .             .           
## state                 -7.593085e-04 -7.547520e-04 -0.0007519739 -7.512948e-04
## fold                  -1.714645e-03 -1.709529e-03 -0.0017066327 -1.704793e-03
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           2.051879e-01  2.056062e-01  0.2059546082  2.060496e-01
## racePctWhite          -3.301702e-02 -3.323765e-02 -0.0335155658 -3.391444e-02
## racePctAsian          -5.804700e-03 -6.547120e-03 -0.0067075288 -7.138295e-03
## racePctHisp            5.019796e-02  5.149375e-02  0.0527761987  5.298069e-02
## agePct12t21            4.987462e-02  4.943221e-02  0.0488182587  4.805493e-02
## agePct12t29           -2.924619e-01 -2.953916e-01 -0.2977925447 -2.995587e-01
## agePct16t24            .             .             .            -1.243071e-05
## agePct65up             2.426943e-02  2.571127e-02  0.0253536135  2.539154e-02
## numbUrban             -7.485739e-02 -7.910050e-02 -0.0826259638 -8.456237e-02
## pctUrban               4.302490e-02  4.317045e-02  0.0433348939  4.336831e-02
## medIncome              .             .            -0.0006755290 -4.436478e-03
## pctWWage              -1.829021e-01 -1.871606e-01 -0.1913528517 -1.925304e-01
## pctWFarmSelf           4.222391e-02  4.282772e-02  0.0432493011  4.363341e-02
## pctWInvInc            -1.458197e-01 -1.475628e-01 -0.1488300924 -1.493832e-01
## pctWSocSec             5.331205e-02  5.280586e-02  0.0540140575  5.506811e-02
## pctWPubAsst           -1.589657e-03 -1.609757e-03 -0.0024515727 -2.626181e-03
## pctWRetire            -8.562873e-02 -8.543700e-02 -0.0855415603 -8.565462e-02
## medFamInc              1.190924e-01  1.262867e-01  0.1329135491  1.382258e-01
## perCapInc              .             1.100181e-04  0.0021214081  3.488980e-03
## whitePerCap           -2.236136e-01 -2.318057e-01 -0.2406761597 -2.456362e-01
## blackPerCap           -2.871316e-02 -2.912858e-02 -0.0295090315 -2.969308e-02
## indianPerCap          -3.023945e-02 -3.052695e-02 -0.0307465278 -3.083547e-02
## AsianPerCap            2.304534e-02  2.283518e-02  0.0226537583  2.259262e-02
## OtherPerCap            4.465730e-02  4.475089e-02  0.0448403170  4.491791e-02
## HispPerCap             2.786643e-02  2.793191e-02  0.0280227721  2.810545e-02
## NumUnderPov            6.083116e-02  6.950652e-02  0.0759609983  7.975543e-02
## PctPopUnderPov        -1.632569e-01 -1.643881e-01 -0.1646155856 -1.652316e-01
## PctLess9thGrade       -9.433590e-02 -9.817891e-02 -0.0999470535 -1.014103e-01
## PctNotHSGrad           4.371720e-02  4.879091e-02  0.0505399860  5.263571e-02
## PctBSorMore            6.549653e-02  6.796524e-02  0.0677066072  6.880872e-02
## PctUnemployed         -7.324282e-03 -6.547900e-03 -0.0056591814 -4.835524e-03
## PctEmploy              2.059523e-01  2.112949e-01  0.2165054508  2.193584e-01
## PctEmplManu           -5.300550e-02 -5.400271e-02 -0.0552174835 -5.568426e-02
## PctEmplProfServ       -1.132101e-02 -1.281416e-02 -0.0140914613 -1.506904e-02
## PctOccupManu           6.417984e-02  6.548855e-02  0.0675012292  6.798299e-02
## PctOccupMgmtProf       5.778440e-02  6.095105e-02  0.0656791504  6.754868e-02
## MalePctDivorce         1.874566e-01  1.893327e-01  0.1920511720  1.945215e-01
## MalePctNevMarr         1.864792e-01  1.894503e-01  0.1919624588  1.937753e-01
## FemalePctDiv          -1.564725e-01 -1.583872e-01 -0.1586552855 -1.580912e-01
## TotalPctDiv           -7.689204e-05 -1.853254e-03 -0.0058203985 -9.645130e-03
## PersPerFam            -6.214724e-03 -1.015251e-02 -0.0153370600 -2.077453e-02
## PctFam2Par             .             .             .             .           
## PctKids2Par           -2.773704e-01 -2.793664e-01 -0.2821413451 -2.827081e-01
## PctYoungKids2Par      -3.231920e-02 -3.229255e-02 -0.0318843503 -3.201485e-02
## PctTeen2Par           -5.276104e-03 -5.727498e-03 -0.0058449703 -6.412245e-03
## PctWorkMomYoungKids    4.092143e-02  4.204442e-02  0.0427347135  4.387266e-02
## PctWorkMom            -1.745202e-01 -1.758042e-01 -0.1767239158 -1.781206e-01
## NumIlleg              -1.134876e-01 -1.186304e-01 -0.1219289807 -1.250156e-01
## PctIlleg               1.290445e-01  1.275580e-01  0.1263375821  1.256202e-01
## NumImmig              -1.205373e-01 -1.219829e-01 -0.1233340965 -1.240902e-01
## PctImmigRecent         1.629879e-02  1.828270e-02  0.0198117417  2.092148e-02
## PctImmigRec5          -8.237271e-03 -8.737599e-03 -0.0087899688 -9.073879e-03
## PctImmigRec8          -8.185966e-03 -1.047567e-02 -0.0123455541 -1.354140e-02
## PctImmigRec10          8.269724e-06  1.579240e-03  0.0026210902  3.660242e-03
## PctRecentImmig        -2.431181e-02 -3.011914e-02 -0.0346105169 -3.776982e-02
## PctRecImmig5           .            -1.806593e-05 -0.0002404767 -6.703422e-04
## PctRecImmig8           5.392462e-02  6.081313e-02  0.0664228291  6.962971e-02
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly      -2.137423e-02 -2.353598e-02 -0.0251633321 -2.614371e-02
## PctNotSpeakEnglWell   -1.041574e-01 -1.101182e-01 -0.1148376889 -1.181197e-01
## PctLargHouseFam       -7.206084e-02 -7.173197e-02 -0.0680578064 -6.560698e-02
## PctLargHouseOccup     -5.139653e-02 -5.389709e-02 -0.0589833092 -6.262877e-02
## PersPerOccupHous       4.109163e-01  4.275774e-01  0.4446841387  4.568942e-01
## PersPerOwnOccHous     -1.657407e-01 -1.717967e-01 -0.1772284783 -1.795500e-01
## PersPerRentOccHous    -1.086687e-01 -1.140552e-01 -0.1198305564 -1.234752e-01
## PctPersOwnOccup       -1.355217e-01 -1.402396e-01 -0.1459935428 -1.508629e-01
## PctPersDenseHous       1.822144e-01  1.850003e-01  0.1867009906  1.889711e-01
## PctHousLess3BR         6.847543e-02  7.073379e-02  0.0726134016  7.343892e-02
## MedNumBR               1.638261e-02  1.709856e-02  0.0177175220  1.813372e-02
## HousVacant             1.640983e-01  1.637814e-01  0.1637179825  1.639898e-01
## PctHousOccup          -5.271444e-02 -5.247025e-02 -0.0522059108 -5.203791e-02
## PctHousOwnOcc          1.971457e-05  2.107619e-03  0.0054737244  8.626714e-03
## PctVacantBoarded       5.685726e-02  5.715664e-02  0.0573575548  5.752851e-02
## PctVacMore6Mos        -6.322005e-02 -6.382018e-02 -0.0644271839 -6.482102e-02
## MedYrHousBuilt        -1.301999e-02 -1.340668e-02 -0.0137186953 -1.420625e-02
## PctHousNoPhone         2.421645e-02  2.366848e-02  0.0232872252  2.289342e-02
## PctWOFullPlumb        -1.045307e-02 -1.079476e-02 -0.0110567532 -1.131326e-02
## OwnOccLowQuart        -1.625690e-01 -1.717060e-01 -0.1812097628 -1.879260e-01
## OwnOccMedVal           8.362070e-03  1.348703e-02  0.0182812234  2.141435e-02
## OwnOccHiQuart          6.536855e-02  6.962135e-02  0.0742684039  7.732317e-02
## RentLowQ              -2.194515e-01 -2.207602e-01 -0.2227353607 -2.238269e-01
## RentMedian             .             .             .             .           
## RentHighQ             -1.308672e-02 -1.994120e-02 -0.0261650181 -2.972208e-02
## MedRent                2.628651e-01  2.699731e-01  0.2775095786  2.824764e-01
## MedRentPctHousInc      4.590865e-02  4.597354e-02  0.0459472598  4.585582e-02
## MedOwnCostPctInc      -3.845785e-02 -3.900821e-02 -0.0393428493 -3.954465e-02
## MedOwnCostPctIncNoMtg -7.613523e-02 -7.612078e-02 -0.0761626666 -7.626839e-02
## NumInShelters          1.152085e-01  1.169768e-01  0.1183639670  1.196441e-01
## NumStreet              1.847181e-01  1.847378e-01  0.1845117670  1.844406e-01
## PctForeignBorn         7.687521e-02  7.782534e-02  0.0787112410  8.015343e-02
## PctBornSameState       .             .             0.0001057004  1.358908e-04
## PctSameHouse85         .             .             .             .           
## PctSameCity85          2.325871e-02  2.305595e-02  0.0227755808  2.249099e-02
## PctSameState85         1.451187e-02  1.471921e-02  0.0149551403  1.535050e-02
## LandArea               1.921605e-02  1.990020e-02  0.0204896864  2.080440e-02
## PopDens               -3.733786e-03 -3.734927e-03 -0.0037057168 -3.887051e-03
## PctUsePubTrans        -3.584148e-02 -3.639642e-02 -0.0368524405 -3.702668e-02
##                                                                              
## (Intercept)            0.6439585039  0.6426140916  0.6423606301  0.6429281231
## (Intercept)            .             .             .             .           
## state                 -0.0007497310 -0.0007483362 -0.0007480608 -0.0007479233
## fold                  -0.0017020464 -0.0016976368 -0.0016953125 -0.0016946590
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.2060866309  0.2062172506  0.2063231387  0.2062617077
## racePctWhite          -0.0344663366 -0.0348633977 -0.0353247687 -0.0359000176
## racePctAsian          -0.0074368256 -0.0072634462 -0.0070853232 -0.0071024570
## racePctHisp            0.0536673056  0.0551771438  0.0559803037  0.0561067630
## agePct12t21            0.0491940948  0.0532217244  0.0567605824  0.0590560207
## agePct12t29           -0.2989971198 -0.2971941944 -0.2951388281 -0.2934211027
## agePct16t24           -0.0037444244 -0.0113497229 -0.0181558206 -0.0229648376
## agePct65up             0.0260583721  0.0263636585  0.0257823589  0.0253348348
## numbUrban             -0.0869926716 -0.0898645962 -0.0915495658 -0.0927423119
## pctUrban               0.0434915788  0.0436995047  0.0437992182  0.0438429215
## medIncome             -0.0111483651 -0.0229626345 -0.0345655352 -0.0433848879
## pctWWage              -0.1940869097 -0.1963081687 -0.1977177891 -0.1985218797
## pctWFarmSelf           0.0439994359  0.0443370231  0.0445932967  0.0447962672
## pctWInvInc            -0.1495017577 -0.1492390673 -0.1491535096 -0.1492895100
## pctWSocSec             0.0556896652  0.0566470422  0.0582614928  0.0595206269
## pctWPubAsst           -0.0027144402 -0.0031925067 -0.0037564152 -0.0041386451
## pctWRetire            -0.0857110621 -0.0855876819 -0.0856587560 -0.0858034565
## medFamInc              0.1456174599  0.1567766105  0.1662883889  0.1729483446
## perCapInc              0.0045361971  0.0066596596  0.0098568372  0.0126325351
## whitePerCap           -0.2508718404 -0.2573394073 -0.2625682830 -0.2662520532
## blackPerCap           -0.0298509412 -0.0300610406 -0.0301962006 -0.0303053999
## indianPerCap          -0.0309499101 -0.0310946988 -0.0311641611 -0.0312034078
## AsianPerCap            0.0225203052  0.0223832589  0.0222866241  0.0222235702
## OtherPerCap            0.0449631195  0.0449694720  0.0449889194  0.0450258583
## HispPerCap             0.0282087428  0.0283512696  0.0285336131  0.0286691188
## NumUnderPov            0.0843300176  0.0895955133  0.0933486321  0.0961148710
## PctPopUnderPov        -0.1665135901 -0.1679199782 -0.1690165017 -0.1700098993
## PctLess9thGrade       -0.1029581664 -0.1036664422 -0.1036183845 -0.1038330409
## PctNotHSGrad           0.0546599159  0.0554194270  0.0549646526  0.0549120508
## PctBSorMore            0.0701387752  0.0697443674  0.0684348755  0.0678942038
## PctUnemployed         -0.0041102275 -0.0032104084 -0.0024414849 -0.0019230059
## PctEmploy              0.2226724384  0.2270992280  0.2304145182  0.2326436192
## PctEmplManu           -0.0563481931 -0.0572606461 -0.0579355385 -0.0583238973
## PctEmplProfServ       -0.0160058423 -0.0166872432 -0.0171595682 -0.0174910983
## PctOccupManu           0.0687031715  0.0701506062  0.0712228640  0.0717419502
## PctOccupMgmtProf       0.0694504934  0.0729468229  0.0762213747  0.0782396597
## MalePctDivorce         0.1979303259  0.2037481285  0.2094541766  0.2140508576
## MalePctNevMarr         0.1961337610  0.1992210402  0.2014794318  0.2030105216
## FemalePctDiv          -0.1568850255 -0.1539023701 -0.1501550862 -0.1470796217
## TotalPctDiv           -0.0149847291 -0.0241079712 -0.0338023030 -0.0416601826
## PersPerFam            -0.0266199495 -0.0348018510 -0.0441540214 -0.0520522290
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2836773235 -0.2856707243 -0.2874047559 -0.2885077088
## PctYoungKids2Par      -0.0321686783 -0.0319355582 -0.0318822354 -0.0320102027
## PctTeen2Par           -0.0067653194 -0.0068320073 -0.0068061836 -0.0068263618
## PctWorkMomYoungKids    0.0447303987  0.0453286467  0.0458586942  0.0464181538
## PctWorkMom            -0.1793969686 -0.1805868288 -0.1816497557 -0.1826148975
## NumIlleg              -0.1277414597 -0.1299894150 -0.1319100641 -0.1337893395
## PctIlleg               0.1248944824  0.1241519281  0.1236126198  0.1232254876
## NumImmig              -0.1248087293 -0.1257254079 -0.1265281215 -0.1271209568
## PctImmigRecent         0.0219028930  0.0229409293  0.0236492926  0.0242170744
## PctImmigRec5          -0.0087409890 -0.0082217343 -0.0078465308 -0.0075450351
## PctImmigRec8          -0.0149894732 -0.0165093797 -0.0176843499 -0.0187095199
## PctImmigRec10          0.0045380554  0.0053715040  0.0061898363  0.0068996785
## PctRecentImmig        -0.0409450221 -0.0438918338 -0.0455534817 -0.0467582211
## PctRecImmig5          -0.0017949790 -0.0045495574 -0.0081281771 -0.0113577045
## PctRecImmig8           0.0731975599  0.0781980671  0.0828825625  0.0868047177
## PctRecImmig10          .             .             .             .           
## PctSpeakEnglOnly      -0.0267318477 -0.0268103591 -0.0268825481 -0.0271382363
## PctNotSpeakEnglWell   -0.1213915087 -0.1245176463 -0.1260212460 -0.1268322876
## PctLargHouseFam       -0.0626028084 -0.0562599146 -0.0486943987 -0.0430409769
## PctLargHouseOccup     -0.0671378666 -0.0749402606 -0.0828666680 -0.0886835088
## PersPerOccupHous       0.4703819804  0.4889110940  0.5058046820  0.5181712152
## PersPerOwnOccHous     -0.1819741853 -0.1851498587 -0.1870524458 -0.1876810550
## PersPerRentOccHous    -0.1277862367 -0.1342748150 -0.1399662095 -0.1439407533
## PctPersOwnOccup       -0.1571405884 -0.1669267547 -0.1764514657 -0.1839081017
## PctPersDenseHous       0.1908188617  0.1916129134  0.1917546503  0.1922095324
## PctHousLess3BR         0.0743945700  0.0753599984  0.0761338031  0.0766845923
## MedNumBR               0.0185782008  0.0190039218  0.0193358222  0.0196242523
## HousVacant             0.1640400434  0.1639850441  0.1639736384  0.1641505352
## PctHousOccup          -0.0518367506 -0.0516606766 -0.0514928494 -0.0513274170
## PctHousOwnOcc          0.0137283836  0.0225505843  0.0311804737  0.0380388037
## PctVacantBoarded       0.0576731297  0.0577537518  0.0577275241  0.0577237555
## PctVacMore6Mos        -0.0652554084 -0.0658134531 -0.0662496433 -0.0665898193
## MedYrHousBuilt        -0.0146125963 -0.0148364678 -0.0151298576 -0.0154863746
## PctHousNoPhone         0.0230060537  0.0234935756  0.0237489745  0.0238941847
## PctWOFullPlumb        -0.0115247599 -0.0117134619 -0.0118998628 -0.0120411074
## OwnOccLowQuart        -0.1950086165 -0.2041602475 -0.2122619567 -0.2182488911
## OwnOccMedVal           0.0254667807  0.0316413870  0.0378546542  0.0430814200
## OwnOccHiQuart          0.0802674680  0.0832326493  0.0846599500  0.0850248033
## RentLowQ              -0.2248960210 -0.2264059810 -0.2278766740 -0.2289320035
## RentMedian             .             .             .             .           
## RentHighQ             -0.0334759855 -0.0378194427 -0.0406555774 -0.0423197975
## MedRent                0.2878955401  0.2952091145  0.3012355309  0.3052389445
## MedRentPctHousInc      0.0457448336  0.0453998959  0.0451044558  0.0449379104
## MedOwnCostPctInc      -0.0398491343 -0.0400306432 -0.0401482093 -0.0402906017
## MedOwnCostPctIncNoMtg -0.0762628560 -0.0761606720 -0.0761248508 -0.0760700065
## NumInShelters          0.1206266024  0.1213516819  0.1218873946  0.1224158230
## NumStreet              0.1843333515  0.1839710527  0.1836312011  0.1834456782
## PctForeignBorn         0.0823422552  0.0850161673  0.0866489777  0.0875976550
## PctBornSameState       0.0004194918  0.0012785559  0.0019606451  0.0023482671
## PctSameHouse85         .             .             .             .           
## PctSameCity85          0.0222275088  0.0219919152  0.0217503306  0.0215291714
## PctSameState85         0.0154819880  0.0150756857  0.0148968416  0.0149556310
## LandArea               0.0211053997  0.0213975010  0.0215403485  0.0215990175
## PopDens               -0.0040723158 -0.0040986980 -0.0042775344 -0.0045173256
## PctUsePubTrans        -0.0373038275 -0.0375778394 -0.0376896405 -0.0377591897
##                                                                              
## (Intercept)            0.6431619678  0.6437510032  6.441303e-01  6.441439e-01
## (Intercept)            .             .             .             .           
## state                 -0.0007484138 -0.0007495898 -7.505425e-04 -7.511411e-04
## fold                  -0.0016933005 -0.0016926646 -1.692252e-03 -1.691224e-03
## population             .             .             .             .           
## householdsize          .             .             .             .           
## racepctblack           0.2064716771  0.2067618176  2.069468e-01  2.070684e-01
## racePctWhite          -0.0364062029 -0.0369501425 -3.752941e-02 -3.818064e-02
## racePctAsian          -0.0069341451 -0.0067526378 -6.697031e-03 -6.754903e-03
## racePctHisp            0.0566593898  0.0571397612  5.740287e-02  5.739789e-02
## agePct12t21            0.0622126408  0.0649795783  6.720808e-02  6.959582e-02
## agePct12t29           -0.2922777602 -0.2914778135 -2.904494e-01 -2.886913e-01
## agePct16t24           -0.0284113771 -0.0332135625 -3.745013e-02 -4.227114e-02
## agePct65up             0.0242772318  0.0225890648  2.129886e-02  2.053856e-02
## numbUrban             -0.0941519216 -0.0953914267 -9.651889e-02 -9.763383e-02
## pctUrban               0.0439096107  0.0439574440  4.399306e-02  4.401637e-02
## medIncome             -0.0528916566 -0.0614531813 -6.864235e-02 -7.652969e-02
## pctWWage              -0.1995333632 -0.2001186552 -2.004341e-01 -2.008633e-01
## pctWFarmSelf           0.0449651179  0.0450751598  4.517958e-02  4.530967e-02
## pctWInvInc            -0.1493168098 -0.1494223158 -1.495957e-01 -1.497703e-01
## pctWSocSec             0.0612085270  0.0634432861  6.539338e-02  6.713604e-02
## pctWPubAsst           -0.0046288372 -0.0050764502 -5.374260e-03 -5.599957e-03
## pctWRetire            -0.0859170804 -0.0860662390 -8.617242e-02 -8.623887e-02
## medFamInc              0.1801036484  0.1864533120  1.918797e-01  1.978299e-01
## perCapInc              0.0167517149  0.0219604883  2.694720e-02  3.223201e-02
## whitePerCap           -0.2712725452 -0.2767381475 -2.818640e-01 -2.871392e-01
## blackPerCap           -0.0304806773 -0.0306588814 -3.082806e-02 -3.100683e-02
## indianPerCap          -0.0312441107 -0.0312616962 -3.127996e-02 -3.130257e-02
## AsianPerCap            0.0221395728  0.0220364758  2.194043e-02  2.184087e-02
## OtherPerCap            0.0450595075  0.0451057342  4.515886e-02  4.520917e-02
## HispPerCap             0.0288470635  0.0290051555  2.911580e-02  2.919939e-02
## NumUnderPov            0.0993315776  0.1019967441  1.040515e-01  1.061063e-01
## PctPopUnderPov        -0.1709790722 -0.1717014593 -1.722744e-01 -1.728436e-01
## PctLess9thGrade       -0.1037902531 -0.1034873568 -1.033310e-01 -1.034319e-01
## PctNotHSGrad           0.0544567369  0.0537129816  5.324332e-02  5.313367e-02
## PctBSorMore            0.0668071979  0.0653623365  6.439885e-02  6.378410e-02
## PctUnemployed         -0.0014181114 -0.0009617028 -5.962313e-04 -1.935730e-04
## PctEmploy              0.2351748695  0.2372759280  2.390306e-01  2.410012e-01
## PctEmplManu           -0.0589329924 -0.0594819027 -5.989156e-02 -6.021244e-02
## PctEmplProfServ       -0.0178742928 -0.0182695991 -1.861610e-02 -1.895135e-02
## PctOccupManu           0.0725858083  0.0732737470  7.368771e-02  7.389381e-02
## PctOccupMgmtProf       0.0809306175  0.0836426639  8.573976e-02  8.755518e-02
## MalePctDivorce         0.2195678028  0.2251049398  2.303338e-01  2.364768e-01
## MalePctNevMarr         0.2048294833  0.2064292210  2.077971e-01  2.092106e-01
## FemalePctDiv          -0.1430657289 -0.1386075522 -1.342366e-01 -1.288077e-01
## TotalPctDiv           -0.0512488697 -0.0613658449 -7.099067e-02 -8.229433e-02
## PersPerFam            -0.0604763384 -0.0689606669 -7.656628e-02 -8.460587e-02
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2898414928 -0.2909535624 -2.918290e-01 -2.926854e-01
## PctYoungKids2Par      -0.0320367322 -0.0320436495 -3.208564e-02 -3.213197e-02
## PctTeen2Par           -0.0067094941 -0.0066126937 -6.548104e-03 -6.467638e-03
## PctWorkMomYoungKids    0.0466834228  0.0469491131  4.721366e-02  4.751836e-02
## PctWorkMom            -0.1833239966 -0.1838949760 -1.843963e-01 -1.849411e-01
## NumIlleg              -0.1353588889 -0.1366722832 -1.377698e-01 -1.389271e-01
## PctIlleg               0.1228054787  0.1223360084  1.219163e-01  1.215175e-01
## NumImmig              -0.1277205149 -0.1282159857 -1.286068e-01 -1.289798e-01
## PctImmigRecent         0.0247005557  0.0251144635  2.548354e-02  2.580330e-02
## PctImmigRec5          -0.0070538743 -0.0064163002 -5.674136e-03 -4.697010e-03
## PctImmigRec8          -0.0198256406 -0.0210434326 -2.224962e-02 -2.351968e-02
## PctImmigRec10          0.0075652843  0.0081888257  8.706427e-03  9.149377e-03
## PctRecentImmig        -0.0477618046 -0.0485217706 -4.915024e-02 -4.953759e-02
## PctRecImmig5          -0.0158764009 -0.0212221290 -2.665056e-02 -3.311984e-02
## PctRecImmig8           0.0921885118  0.0984274285  1.045565e-01  1.116331e-01
## PctRecImmig10          .             .            -9.853331e-06 -2.635694e-04
## PctSpeakEnglOnly      -0.0272261948 -0.0273227600 -2.754541e-02 -2.820264e-02
## PctNotSpeakEnglWell   -0.1272348128 -0.1274302602 -1.277437e-01 -1.282593e-01
## PctLargHouseFam       -0.0362813493 -0.0292810804 -2.328129e-02 -1.710110e-02
## PctLargHouseOccup     -0.0950303804 -0.1011352178 -1.064527e-01 -1.122858e-01
## PersPerOccupHous       0.5309952567  0.5424667449  5.520347e-01  5.620922e-01
## PersPerOwnOccHous     -0.1882303084 -0.1881683154 -1.875350e-01 -1.863141e-01
## PersPerRentOccHous    -0.1481542044 -0.1519464955 -1.551872e-01 -1.587484e-01
## PctPersOwnOccup       -0.1928242564 -0.2022545993 -2.113657e-01 -2.220139e-01
## PctPersDenseHous       0.1914758425  0.1904545255  1.898853e-01  1.898021e-01
## PctHousLess3BR         0.0774895055  0.0783161114  7.905601e-02  7.976322e-02
## MedNumBR               0.0199049390  0.0201778753  2.044303e-02  2.072671e-02
## HousVacant             0.1641881374  0.1644054353  1.647507e-01  1.651033e-01
## PctHousOccup          -0.0511587067 -0.0509606031 -5.073512e-02 -5.044649e-02
## PctHousOwnOcc          0.0462729508  0.0548539776  6.332474e-02  7.353580e-02
## PctVacantBoarded       0.0576301349  0.0575244422  5.743889e-02  5.732549e-02
## PctVacMore6Mos        -0.0669517272 -0.0673004503 -6.760631e-02 -6.789337e-02
## MedYrHousBuilt        -0.0157358951 -0.0160375200 -1.635192e-02 -1.668233e-02
## PctHousNoPhone         0.0240982558  0.0242128327  2.427394e-02  2.426369e-02
## PctWOFullPlumb        -0.0121954156 -0.0123321243 -1.245596e-02 -1.258925e-02
## OwnOccLowQuart        -0.2253420511 -0.2332674440 -2.408792e-01 -2.491505e-01
## OwnOccMedVal           0.0500766820  0.0580901484  6.595726e-02  7.487016e-02
## OwnOccHiQuart          0.0846558111  0.0838894725  8.299829e-02  8.168822e-02
## RentLowQ              -0.2300776582 -0.2311399909 -2.320028e-01 -2.327774e-01
## RentMedian             .             .             .             .           
## RentHighQ             -0.0437433112 -0.0449600112 -4.612626e-02 -4.744718e-02
## MedRent                0.3091902233  0.3126896802  3.156187e-01  3.186232e-01
## MedRentPctHousInc      0.0447505324  0.0446432926  4.458196e-02  4.448750e-02
## MedOwnCostPctInc      -0.0404406633 -0.0405386913 -4.060207e-02 -4.064926e-02
## MedOwnCostPctIncNoMtg -0.0760074780 -0.0759484294 -7.587687e-02 -7.577764e-02
## NumInShelters          0.1227853741  0.1231879187  1.236665e-01  1.241919e-01
## NumStreet              0.1831026377  0.1826949419  1.823310e-01  1.819602e-01
## PctForeignBorn         0.0882516458  0.0884269629  8.858944e-02  8.866644e-02
## PctBornSameState       0.0030141577  0.0035768578  4.062487e-03  4.589697e-03
## PctSameHouse85         .             .             .            -3.387379e-05
## PctSameCity85          0.0213811254  0.0212682267  2.118568e-02  2.108748e-02
## PctSameState85         0.0147628132  0.0146639113  1.461218e-02  1.455963e-02
## LandArea               0.0216275294  0.0215865070  2.156494e-02  2.153934e-02
## PopDens               -0.0047534506 -0.0050321468 -5.295247e-03 -5.578175e-03
## PctUsePubTrans        -0.0378813251 -0.0379857074 -3.806879e-02 -3.811176e-02
##                                                                              
## (Intercept)            0.6437157723  6.430282e-01  0.6419464266  0.6408235533
## (Intercept)            .             .             .             .           
## state                 -0.0007513108 -7.512659e-04 -0.0007511509 -0.0007506671
## fold                  -0.0016899221 -1.688432e-03 -0.0016866000 -0.0016857428
## population             .             3.840233e-05  0.0008094939  0.0022439713
## householdsize          .             .             .            -0.0006194785
## racepctblack           0.2071962843  2.072590e-01  0.2072591834  0.2072562973
## racePctWhite          -0.0387815959 -3.936243e-02 -0.0399766976 -0.0405526189
## racePctAsian          -0.0068525453 -6.971674e-03 -0.0070995973 -0.0071686447
## racePctHisp            0.0573115689  5.719880e-02  0.0570717676  0.0568855591
## agePct12t21            0.0718994265  7.410214e-02  0.0764293787  0.0787108465
## agePct12t29           -0.2865310347 -2.842011e-01 -0.2815498516 -0.2791714511
## agePct16t24           -0.0472023771 -5.194927e-02 -0.0569429271 -0.0615555831
## agePct65up             0.0201570869  2.003983e-02  0.0201103472  0.0201796961
## numbUrban             -0.0986275506 -9.952599e-02 -0.1010059820 -0.1029336825
## pctUrban               0.0440327264  4.404487e-02  0.0440786284  0.0441236852
## medIncome             -0.0842072457 -9.127694e-02 -0.0984792118 -0.1050694651
## pctWWage              -0.2013030086 -2.017032e-01 -0.2020968510 -0.2025111356
## pctWFarmSelf           0.0454475216  4.558172e-02  0.0457159758  0.0458864435
## pctWInvInc            -0.1499180215 -1.500608e-01 -0.1501956056 -0.1502254234
## pctWSocSec             0.0685463441  6.961593e-02  0.0705494759  0.0714181573
## pctWPubAsst           -0.0057691021 -5.867352e-03 -0.0059451867 -0.0061491203
## pctWRetire            -0.0862465067 -8.622742e-02 -0.0862063988 -0.0862238698
## medFamInc              0.2037480052  2.091890e-01  0.2147262117  0.2199484426
## perCapInc              0.0373568028  4.206622e-02  0.0468904943  0.0509601389
## whitePerCap           -0.2921616584 -2.966595e-01 -0.3010978634 -0.3047663796
## blackPerCap           -0.0311736295 -3.132194e-02 -0.0314771876 -0.0316130046
## indianPerCap          -0.0313399777 -3.138318e-02 -0.0314293574 -0.0314793427
## AsianPerCap            0.0217359813  2.163852e-02  0.0215332541  0.0214373548
## OtherPerCap            0.0452426981  4.526749e-02  0.0452861509  0.0453135634
## HispPerCap             0.0292714949  2.933655e-02  0.0293987245  0.0294193118
## NumUnderPov            0.1079821831  1.096335e-01  0.1111817386  0.1124679499
## PctPopUnderPov        -0.1732782953 -1.736398e-01 -0.1739645632 -0.1742437838
## PctLess9thGrade       -0.1036262584 -1.038500e-01 -0.1040530594 -0.1044597494
## PctNotHSGrad           0.0532132702  5.334081e-02  0.0534620653  0.0536110602
## PctBSorMore            0.0633075589  6.293899e-02  0.0625313673  0.0618697154
## PctUnemployed          .             .             .             .           
## PctEmploy              0.2427985217  2.442878e-01  0.2457269858  0.2469122966
## PctEmplManu           -0.0604596259 -6.065496e-02 -0.0608237292 -0.0609547866
## PctEmplProfServ       -0.0192588388 -1.955001e-02 -0.0198197586 -0.0199612351
## PctOccupManu           0.0739793504  7.401712e-02  0.0739999077  0.0739477238
## PctOccupMgmtProf       0.0891166032  9.044680e-02  0.0917384202  0.0929550509
## MalePctDivorce         0.2428204136  2.490087e-01  0.2557100285  0.2621158991
## MalePctNevMarr         0.2105082806  2.116442e-01  0.2127197841  0.2139417161
## FemalePctDiv          -0.1228130314 -1.166924e-01 -0.1098261406 -0.1033702221
## TotalPctDiv           -0.0942314751 -1.060456e-01 -0.1189985968 -0.1312397980
## PersPerFam            -0.0920226203 -9.843935e-02 -0.1046438603 -0.1087911516
## PctFam2Par             .             .             .             .           
## PctKids2Par           -0.2934908913 -2.942372e-01 -0.2950072968 -0.2958165002
## PctYoungKids2Par      -0.0321717732 -3.220920e-02 -0.0322173747 -0.0322285759
## PctTeen2Par           -0.0063968980 -6.335639e-03 -0.0062758051 -0.0062323800
## PctWorkMomYoungKids    0.0478009904  4.806886e-02  0.0483277923  0.0485934103
## PctWorkMom            -0.1854196885 -1.858381e-01 -0.1862158408 -0.1865811919
## NumIlleg              -0.1400672698 -1.411031e-01 -0.1420809944 -0.1430287086
## PctIlleg               0.1211636957  1.208466e-01  0.1205424404  0.1203415760
## NumImmig              -0.1293597335 -1.297092e-01 -0.1300570607 -0.1302800584
## PctImmigRecent         0.0259798363  2.604755e-02  0.0260496597  0.0259781060
## PctImmigRec5          -0.0036458331 -2.622584e-03 -0.0015173423 -0.0004917267
## PctImmigRec8          -0.0247519514 -2.599087e-02 -0.0273919972 -0.0287143645
## PctImmigRec10          0.0096444916  1.025156e-02  0.0110079961  0.0118036515
## PctRecentImmig        -0.0494042854 -4.892213e-02 -0.0482125515 -0.0474537835
## PctRecImmig5          -0.0399121337 -4.641694e-02 -0.0532249495 -0.0593979117
## PctRecImmig8           0.1191800068  1.268310e-01  0.1354560953  0.1436015173
## PctRecImmig10         -0.0018057120 -4.350211e-03 -0.0079924357 -0.0119903412
## PctSpeakEnglOnly      -0.0287775671 -2.919985e-02 -0.0294960837 -0.0294715439
## PctNotSpeakEnglWell   -0.1287736999 -1.291938e-01 -0.1295296437 -0.1292631982
## PctLargHouseFam       -0.0111091940 -5.544514e-03  .             .           
## PctLargHouseOccup     -0.1181738081 -1.238318e-01 -0.1296428962 -0.1301560432
## PersPerOccupHous       0.5716139092  5.800249e-01  0.5881719292  0.5947222787
## PersPerOwnOccHous     -0.1847773190 -1.830709e-01 -0.1809987488 -0.1790840062
## PersPerRentOccHous    -0.1623866516 -1.658655e-01 -0.1695010153 -0.1729601952
## PctPersOwnOccup       -0.2334159572 -2.448419e-01 -0.2574497474 -0.2696117726
## PctPersDenseHous       0.1899572217  1.902066e-01  0.1905107392  0.1910624390
## PctHousLess3BR         0.0804488232  8.107809e-02  0.0816986470  0.0822924405
## MedNumBR               0.0210057442  2.126011e-02  0.0215140411  0.0217597147
## HousVacant             0.1654292234  1.657247e-01  0.1659847735  0.1662061689
## PctHousOccup          -0.0501887111 -4.998110e-02 -0.0497830671 -0.0496388375
## PctHousOwnOcc          0.0847248754  9.609352e-02  0.1087333941  0.1209275261
## PctVacantBoarded       0.0572219295  5.712866e-02  0.0570228999  0.0569273628
## PctVacMore6Mos        -0.0681297599 -6.834117e-02 -0.0685456276 -0.0687357913
## MedYrHousBuilt        -0.0170644292 -1.743054e-02 -0.0177973217 -0.0181552098
## PctHousNoPhone         0.0242137009  2.414432e-02  0.0240471071  0.0239894331
## PctWOFullPlumb        -0.0127134424 -1.282979e-02 -0.0129272602 -0.0130122034
## OwnOccLowQuart        -0.2571913250 -2.645806e-01 -0.2721749819 -0.2789252728
## OwnOccMedVal           0.0838830901  9.245637e-02  0.1015198251  0.1098004572
## OwnOccHiQuart          0.0800174320  7.813511e-02  0.0758917355  0.0736493650
## RentLowQ              -0.2334726793 -2.340957e-01 -0.2346841098 -0.2351755342
## RentMedian             .             .             .             .           
## RentHighQ             -0.0486867707 -4.975566e-02 -0.0507849136 -0.0516104816
## MedRent                0.3213815243  3.238200e-01  0.3261880002  0.3282416082
## MedRentPctHousInc      0.0443692525  4.424922e-02  0.0441206488  0.0439921130
## MedOwnCostPctInc      -0.0407234194 -4.077784e-02 -0.0408032868 -0.0407792796
## MedOwnCostPctIncNoMtg -0.0756484082 -7.551485e-02 -0.0753770448 -0.0752639993
## NumInShelters          0.1247449443  1.252330e-01  0.1256753648  0.1261780107
## NumStreet              0.1816385147  1.813664e-01  0.1810945543  0.1809075125
## PctForeignBorn         0.0894527566  9.054603e-02  0.0918547484  0.0930915376
## PctBornSameState       0.0051442315  5.677073e-03  0.0062309039  0.0067051515
## PctSameHouse85        -0.0003966790 -7.350660e-04 -0.0010280482 -0.0014033613
## PctSameCity85          0.0210810688  2.109566e-02  0.0211028529  0.0211223315
## PctSameState85         0.0145248614  1.447564e-02  0.0144083050  0.0143679687
## LandArea               0.0215119363  2.147860e-02  0.0213960065  0.0212337369
## PopDens               -0.0058476895 -6.095109e-03 -0.0063428134 -0.0066016837
## PctUsePubTrans        -0.0381366395 -3.815726e-02 -0.0381684055 -0.0381300366
##                                                                             
## (Intercept)            0.640162912  0.6396514312  0.6389222528  6.381134e-01
## (Intercept)            .            .             .             .           
## state                 -0.000750186 -0.0007498043 -0.0007493858 -7.490090e-04
## fold                  -0.001684710 -0.0016835301 -0.0016821701 -1.680547e-03
## population             0.003943528  0.0055890382  0.0075288033  9.756289e-03
## householdsize         -0.001598585 -0.0024004081 -0.0031083272 -3.696360e-03
## racepctblack           0.207257428  0.2072702725  0.2072764007  2.072782e-01
## racePctWhite          -0.040939585 -0.0412109026 -0.0414788747 -4.172108e-02
## racePctAsian          -0.007278138 -0.0073999813 -0.0075081075 -7.609871e-03
## racePctHisp            0.056812280  0.0567332935  0.0566295878  5.656188e-02
## agePct12t21            0.080661970  0.0821738438  0.0837192537  8.522765e-02
## agePct12t29           -0.277207331 -0.2757266863 -0.2743259215 -2.729257e-01
## agePct16t24           -0.065282631 -0.0681422902 -0.0709741556 -7.373906e-02
## agePct65up             0.020246566  0.0203619258  0.0205459836  2.082221e-02
## numbUrban             -0.105074680 -0.1071511333 -0.1094927792 -1.120711e-01
## pctUrban               0.044187385  0.0442520544  0.0443296465  4.441764e-02
## medIncome             -0.110280480 -0.1143415818 -0.1183541589 -1.222813e-01
## pctWWage              -0.202825763 -0.2030188858 -0.2031796399 -2.033276e-01
## pctWFarmSelf           0.046010109  0.0461062864  0.0462046923  4.629149e-02
## pctWInvInc            -0.150291314 -0.1503650856 -0.1504148722 -1.504511e-01
## pctWSocSec             0.072116347  0.0726613339  0.0732142014  7.371800e-02
## pctWPubAsst           -0.006300486 -0.0063946926 -0.0065098284 -6.623104e-03
## pctWRetire            -0.086237229 -0.0862484298 -0.0862640363 -8.629343e-02
## medFamInc              0.224001239  0.2271264132  0.2301724329  2.330812e-01
## perCapInc              0.053996519  0.0562651367  0.0584262100  6.050345e-02
## whitePerCap           -0.307597340 -0.3097837678 -0.3118616737 -3.138383e-01
## blackPerCap           -0.031712228 -0.0317873812 -0.0318705373 -3.194720e-02
## indianPerCap          -0.031528086 -0.0315733823 -0.0316161953 -3.165808e-02
## AsianPerCap            0.021363069  0.0213025924  0.0212429851  2.118462e-02
## OtherPerCap            0.045324178  0.0453244201  0.0453205090  4.530956e-02
## HispPerCap             0.029452067  0.0294919960  0.0295326261  2.958080e-02
## NumUnderPov            0.113490379  0.1144199097  0.1153912377  1.163300e-01
## PctPopUnderPov        -0.174573402 -0.1748914090 -0.1752467884 -1.756107e-01
## PctLess9thGrade       -0.104747752 -0.1050196971 -0.1053269221 -1.056008e-01
## PctNotHSGrad           0.053819442  0.0540593374  0.0543524493  5.466258e-02
## PctBSorMore            0.061483758  0.0612628675  0.0610684186  6.095129e-02
## PctUnemployed          .            .             0.0001968996  4.716725e-04
## PctEmploy              0.247788159  0.2484799768  0.2492922443  2.501491e-01
## PctEmplManu           -0.061078522 -0.0611854150 -0.0612893601 -6.138780e-02
## PctEmplProfServ       -0.020112175 -0.0202385025 -0.0203672612 -2.050627e-02
## PctOccupManu           0.073913187  0.0739230532  0.0739366712  7.394255e-02
## PctOccupMgmtProf       0.093881138  0.0945959767  0.0953109015  9.597693e-02
## MalePctDivorce         0.267435413  0.2717307415  0.2760360841  2.803305e-01
## MalePctNevMarr         0.214903110  0.2156733181  0.2164357182  2.171116e-01
## FemalePctDiv          -0.098087847 -0.0938502385 -0.0895681916 -8.522830e-02
## TotalPctDiv           -0.141409932 -0.1495906448 -0.1578317275 -1.661156e-01
## PersPerFam            -0.111762987 -0.1139455847 -0.1159333667 -1.179689e-01
## PctFam2Par             .            .             .             .           
## PctKids2Par           -0.296486330 -0.2969995991 -0.2975151228 -2.980165e-01
## PctYoungKids2Par      -0.032205018 -0.0321783501 -0.0321389620 -3.208653e-02
## PctTeen2Par           -0.006165954 -0.0061190926 -0.0060749185 -6.023838e-03
## PctWorkMomYoungKids    0.048789617  0.0489301372  0.0490709605  4.920088e-02
## PctWorkMom            -0.186848682 -0.1870575630 -0.1872794023 -1.874880e-01
## NumIlleg              -0.143734975 -0.1443705584 -0.1450322457 -1.456480e-01
## PctIlleg               0.120237668  0.1201712269  0.1200909712  1.200118e-01
## NumImmig              -0.130439435 -0.1305862466 -0.1307331113 -1.308809e-01
## PctImmigRecent         0.025973312  0.0261432755  0.0263433469  2.655267e-02
## PctImmigRec5           .            .             .             4.354954e-06
## PctImmigRec8          -0.029655079 -0.0302307587 -0.0308842083 -3.159139e-02
## PctImmigRec10          0.012576658  0.0132461316  0.0139733355  1.472649e-02
## PctRecentImmig        -0.046863700 -0.0464487752 -0.0461698143 -4.601301e-02
## PctRecImmig5          -0.064255947 -0.0678635085 -0.0712166599 -7.432714e-02
## PctRecImmig8           0.150636767  0.1563145232  0.1620825440  1.679641e-01
## PctRecImmig10         -0.015685927 -0.0189239951 -0.0224109624 -2.609777e-02
## PctSpeakEnglOnly      -0.029543481 -0.0296913849 -0.0298144731 -2.995173e-02
## PctNotSpeakEnglWell   -0.129396690 -0.1295982080 -0.1297569531 -1.300421e-01
## PctLargHouseFam        .            .             0.0001020045  6.801084e-04
## PctLargHouseOccup     -0.130363873 -0.1305647249 -0.1309442444 -1.317326e-01
## PersPerOccupHous       0.599671800  0.6034034269  0.6068965248  6.102079e-01
## PersPerOwnOccHous     -0.177414759 -0.1760321684 -0.1746680562 -1.732311e-01
## PersPerRentOccHous    -0.175806489 -0.1780597103 -0.1803005560 -1.825095e-01
## PctPersOwnOccup       -0.279813178 -0.2880312805 -0.2962451019 -3.044748e-01
## PctPersDenseHous       0.191542717  0.1919884676  0.1924682080  1.929416e-01
## PctHousLess3BR         0.082621891  0.0828525295  0.0830715476  8.324754e-02
## MedNumBR               0.021959641  0.0221234794  0.0222811426  2.243054e-02
## HousVacant             0.166345399  0.1664449282  0.1665098162  1.665389e-01
## PctHousOccup          -0.049512647 -0.0494289614 -0.0493385784 -4.924736e-02
## PctHousOwnOcc          0.131237396  0.1396180385  0.1480327373  1.564571e-01
## PctVacantBoarded       0.056852469  0.0567921772  0.0567187022  5.664576e-02
## PctVacMore6Mos        -0.068891394 -0.0690330416 -0.0691742102 -6.930167e-02
## MedYrHousBuilt        -0.018471752 -0.0187279814 -0.0189592069 -1.917471e-02
## PctHousNoPhone         0.023946836  0.0239117401  0.0238813153  2.384646e-02
## PctWOFullPlumb        -0.013065217 -0.0131090973 -0.0131541782 -1.319953e-02
## OwnOccLowQuart        -0.284261199 -0.2884415448 -0.2925997766 -2.967107e-01
## OwnOccMedVal           0.116533151  0.1218844623  0.1272277917  1.325271e-01
## OwnOccHiQuart          0.071771160  0.0702854296  0.0688040699  6.733189e-02
## RentLowQ              -0.235590493 -0.2359382068 -0.2362525543 -2.365243e-01
## RentMedian             .            .             .             .           
## RentHighQ             -0.052212442 -0.0526986413 -0.0532046513 -5.370633e-02
## MedRent                0.329891089  0.3312077116  0.3325147633  3.337731e-01
## MedRentPctHousInc      0.043876574  0.0437907190  0.0437006312  4.360638e-02
## MedOwnCostPctInc      -0.040803429 -0.0408614001 -0.0409198995 -4.098247e-02
## MedOwnCostPctIncNoMtg -0.075179674 -0.0751121904 -0.0750635018 -7.501860e-02
## NumInShelters          0.126559187  0.1268824787  0.1271815734  1.274377e-01
## NumStreet              0.180765400  0.1806637014  0.1805559867  1.804432e-01
## PctForeignBorn         0.094134534  0.0949937636  0.0958547660  9.670453e-02
## PctBornSameState       0.007056888  0.0073290288  0.0076033344  7.871984e-03
## PctSameHouse85        -0.001844816 -0.0022640747 -0.0026722194 -3.048682e-03
## PctSameCity85          0.021113062  0.0211104628  0.0210988850  2.107460e-02
## PctSameState85         0.014360966  0.0143733132  0.0143716852  1.437342e-02
## LandArea               0.021133197  0.0210683049  0.0209896008  2.091038e-02
## PopDens               -0.006828834 -0.0069998666 -0.0071616428 -7.318297e-03
## PctUsePubTrans        -0.038116143 -0.0381086564 -0.0380976529 -3.808695e-02
##                                                  
## (Intercept)            6.370785e-01  6.358429e-01
## (Intercept)            .             .           
## state                 -7.486413e-04 -7.481044e-04
## fold                  -1.678673e-03 -1.676658e-03
## population             1.249188e-02  1.590759e-02
## householdsize         -4.224863e-03 -4.735576e-03
## racepctblack           2.072862e-01  2.073014e-01
## racePctWhite          -4.196756e-02 -4.221576e-02
## racePctAsian          -7.722032e-03 -7.818397e-03
## racePctHisp            5.653201e-02  5.652526e-02
## agePct12t21            8.693137e-02  8.867248e-02
## agePct12t29           -2.713600e-01 -2.694641e-01
## agePct16t24           -7.673339e-02 -7.998053e-02
## agePct65up             2.117101e-02  2.162784e-02
## numbUrban             -1.150228e-01 -1.186102e-01
## pctUrban               4.452219e-02  4.464399e-02
## medIncome             -1.264475e-01 -1.310684e-01
## pctWWage              -2.034656e-01 -2.036345e-01
## pctWFarmSelf           4.637592e-02  4.646844e-02
## pctWInvInc            -1.504805e-01 -1.504945e-01
## pctWSocSec             7.417311e-02  7.455176e-02
## pctWPubAsst           -6.690294e-03 -6.743856e-03
## pctWRetire            -8.631835e-02 -8.635247e-02
## medFamInc              2.360470e-01  2.392827e-01
## perCapInc              6.266141e-02  6.501509e-02
## whitePerCap           -3.158314e-01 -3.178799e-01
## blackPerCap           -3.202057e-02 -3.209389e-02
## indianPerCap          -3.170259e-02 -3.174974e-02
## AsianPerCap            2.112218e-02  2.106051e-02
## OtherPerCap            4.529362e-02  4.527864e-02
## HispPerCap             2.962860e-02  2.967572e-02
## NumUnderPov            1.171800e-01  1.180332e-01
## PctPopUnderPov        -1.759942e-01 -1.763677e-01
## PctLess9thGrade       -1.058398e-01 -1.060215e-01
## PctNotHSGrad           5.496988e-02  5.524325e-02
## PctBSorMore            6.087973e-02  6.075375e-02
## PctUnemployed          7.385471e-04  9.857262e-04
## PctEmploy              2.510087e-01  2.518518e-01
## PctEmplManu           -6.147324e-02 -6.154035e-02
## PctEmplProfServ       -2.067473e-02 -2.083660e-02
## PctOccupManu           7.391981e-02  7.384733e-02
## PctOccupMgmtProf       9.664281e-02  9.734355e-02
## MalePctDivorce         2.849532e-01  2.902039e-01
## MalePctNevMarr         2.177425e-01  2.183262e-01
## FemalePctDiv          -8.041956e-02 -7.477760e-02
## TotalPctDiv           -1.751232e-01 -1.854876e-01
## PersPerFam            -1.201504e-01 -1.226384e-01
## PctFam2Par             .             .           
## PctKids2Par           -2.985087e-01 -2.989823e-01
## PctYoungKids2Par      -3.202750e-02 -3.197103e-02
## PctTeen2Par           -5.952228e-03 -5.902423e-03
## PctWorkMomYoungKids    4.934023e-02  4.949449e-02
## PctWorkMom            -1.876947e-01 -1.878912e-01
## NumIlleg              -1.462293e-01 -1.467886e-01
## PctIlleg               1.199112e-01  1.198026e-01
## NumImmig              -1.310505e-01 -1.312371e-01
## PctImmigRecent         2.684605e-02  2.691308e-02
## PctImmigRec5           9.611477e-05  7.203047e-04
## PctImmigRec8          -3.255311e-02 -3.385749e-02
## PctImmigRec10          1.556075e-02  1.645881e-02
## PctRecentImmig        -4.602738e-02 -4.617530e-02
## PctRecImmig5          -7.746231e-02 -8.089490e-02
## PctRecImmig8           1.745130e-01  1.821062e-01
## PctRecImmig10         -3.025767e-02 -3.517662e-02
## PctSpeakEnglOnly      -3.004713e-02 -3.013151e-02
## PctNotSpeakEnglWell   -1.303647e-01 -1.307373e-01
## PctLargHouseFam        1.779460e-03  3.482042e-03
## PctLargHouseOccup     -1.330776e-01 -1.350820e-01
## PersPerOccupHous       6.135559e-01  6.171223e-01
## PersPerOwnOccHous     -1.716013e-01 -1.695223e-01
## PersPerRentOccHous    -1.848546e-01 -1.874281e-01
## PctPersOwnOccup       -3.133836e-01 -3.235568e-01
## PctPersDenseHous       1.934229e-01  1.939231e-01
## PctHousLess3BR         8.341617e-02  8.358244e-02
## MedNumBR               2.257499e-02  2.272677e-02
## HousVacant             1.665456e-01  1.665189e-01
## PctHousOccup          -4.915059e-02 -4.905237e-02
## PctHousOwnOcc          1.655963e-01  1.760360e-01
## PctVacantBoarded       5.656822e-02  5.647869e-02
## PctVacMore6Mos        -6.942569e-02 -6.953543e-02
## MedYrHousBuilt        -1.939269e-02 -1.962627e-02
## PctHousNoPhone         2.381747e-02  2.375352e-02
## PctWOFullPlumb        -1.324877e-02 -1.329620e-02
## OwnOccLowQuart        -3.011107e-01 -3.060642e-01
## OwnOccMedVal           1.381904e-01  1.445948e-01
## OwnOccHiQuart          6.571340e-02  6.382458e-02
## RentLowQ              -2.367745e-01 -2.370258e-01
## RentMedian            -9.559029e-06 -1.952605e-05
## RentHighQ             -5.413085e-02 -5.452863e-02
## MedRent                3.350526e-01  3.363725e-01
## MedRentPctHousInc      4.348571e-02  4.334516e-02
## MedOwnCostPctInc      -4.101505e-02 -4.103653e-02
## MedOwnCostPctIncNoMtg -7.497441e-02 -7.493189e-02
## NumInShelters          1.276634e-01  1.278606e-01
## NumStreet              1.803157e-01  1.801715e-01
## PctForeignBorn         9.760336e-02  9.861857e-02
## PctBornSameState       8.176421e-03  8.470026e-03
## PctSameHouse85        -3.356536e-03 -3.630089e-03
## PctSameCity85          2.104926e-02  2.102069e-02
## PctSameState85         1.434782e-02  1.433711e-02
## LandArea               2.079170e-02  2.065857e-02
## PopDens               -7.496714e-03 -7.669628e-03
## PctUsePubTrans        -3.806240e-02 -3.803492e-02
lasso.mod$lambda
##   [1] 1.719985e-01 1.567186e-01 1.427961e-01 1.301105e-01 1.185519e-01
##   [6] 1.080200e-01 9.842384e-02 8.968014e-02 8.171320e-02 7.445402e-02
##  [11] 6.783973e-02 6.181304e-02 5.632174e-02 5.131827e-02 4.675930e-02
##  [16] 4.260533e-02 3.882039e-02 3.537169e-02 3.222937e-02 2.936620e-02
##  [21] 2.675739e-02 2.438034e-02 2.221445e-02 2.024098e-02 1.844283e-02
##  [26] 1.680442e-02 1.531156e-02 1.395132e-02 1.271193e-02 1.158263e-02
##  [31] 1.055367e-02 9.616107e-03 8.761839e-03 7.983461e-03 7.274232e-03
##  [36] 6.628010e-03 6.039195e-03 5.502690e-03 5.013846e-03 4.568430e-03
##  [41] 4.162583e-03 3.792791e-03 3.455850e-03 3.148842e-03 2.869107e-03
##  [46] 2.614224e-03 2.381983e-03 2.170374e-03 1.977564e-03 1.801883e-03
##  [51] 1.641809e-03 1.495955e-03 1.363058e-03 1.241968e-03 1.131635e-03
##  [56] 1.031104e-03 9.395032e-04 8.560404e-04 7.799921e-04 7.106997e-04
##  [61] 6.475631e-04 5.900354e-04 5.376183e-04 4.898578e-04 4.463401e-04
##  [66] 4.066885e-04 3.705594e-04 3.376400e-04 3.076450e-04 2.803146e-04
##  [71] 2.554122e-04 2.327221e-04 2.120477e-04 1.932100e-04 1.760458e-04
##  [76] 1.604063e-04 1.461563e-04 1.331722e-04 1.213415e-04 1.105619e-04
##  [81] 1.007399e-04 9.179040e-05 8.363599e-05 7.620600e-05 6.943607e-05
##  [86] 6.326756e-05 5.764705e-05 5.252584e-05 4.785959e-05 4.360788e-05
##  [91] 3.973387e-05 3.620403e-05 3.298776e-05 3.005722e-05 2.738702e-05
##  [96] 2.495403e-05 2.273718e-05 2.071727e-05 1.887681e-05 1.719985e-05
plot(lasso.mod,"lambda", label=TRUE)

lasso.mod$lambda[5]
## [1] 0.1185519
log(lasso.mod$lambda[5])
## [1] -2.132405
coef(lasso.mod)[,5]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##           (Intercept)           (Intercept)                 state 
##             0.2951769             0.0000000             0.0000000 
##                  fold            population         householdsize 
##             0.0000000             0.0000000             0.0000000 
##          racepctblack          racePctWhite          racePctAsian 
##             0.0000000             0.0000000             0.0000000 
##           racePctHisp           agePct12t21           agePct12t29 
##             0.0000000             0.0000000             0.0000000 
##           agePct16t24            agePct65up             numbUrban 
##             0.0000000             0.0000000             0.0000000 
##              pctUrban             medIncome              pctWWage 
##             0.0000000             0.0000000             0.0000000 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##             0.0000000             0.0000000             0.0000000 
##           pctWPubAsst            pctWRetire             medFamInc 
##             0.0000000             0.0000000             0.0000000 
##             perCapInc           whitePerCap           blackPerCap 
##             0.0000000             0.0000000             0.0000000 
##          indianPerCap           AsianPerCap           OtherPerCap 
##             0.0000000             0.0000000             0.0000000 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##             0.0000000             0.0000000             0.0000000 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##             0.0000000             0.0000000             0.0000000 
##         PctUnemployed             PctEmploy           PctEmplManu 
##             0.0000000             0.0000000             0.0000000 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##             0.0000000             0.0000000             0.0000000 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##             0.0000000             0.0000000             0.0000000 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##             0.0000000             0.0000000             0.0000000 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##            -0.1411978             0.0000000             0.0000000 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##             0.0000000             0.0000000             0.0000000 
##              PctIlleg              NumImmig        PctImmigRecent 
##             0.1217523             0.0000000             0.0000000 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##             0.0000000             0.0000000             0.0000000 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##             0.0000000             0.0000000             0.0000000 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##             0.0000000             0.0000000             0.0000000 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##             0.0000000             0.0000000             0.0000000 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##             0.0000000             0.0000000             0.0000000 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##             0.0000000             0.0000000             0.0000000 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##             0.0000000             0.0000000             0.0000000 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##             0.0000000             0.0000000             0.0000000 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##             0.0000000             0.0000000             0.0000000 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##             0.0000000             0.0000000             0.0000000 
##            RentMedian             RentHighQ               MedRent 
##             0.0000000             0.0000000             0.0000000 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##             0.0000000             0.0000000             0.0000000 
##         NumInShelters             NumStreet        PctForeignBorn 
##             0.0000000             0.0000000             0.0000000 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##             0.0000000             0.0000000             0.0000000 
##        PctSameState85              LandArea               PopDens 
##             0.0000000             0.0000000             0.0000000 
##        PctUsePubTrans 
##             0.0000000
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(lasso.mod$lambda[5]), col="blue", lwd=4, lty=3)

lasso.mod$lambda[60]
## [1] 0.0007106997
log(lasso.mod$lambda[60])
## [1] -7.249261
coef(lasso.mod)[,60]  # Lambda más grande penaliza más tienden a ser los beta más pequeños
##           (Intercept)           (Intercept)                 state 
##          5.697581e-01          0.000000e+00         -8.302962e-04 
##                  fold            population         householdsize 
##         -1.646556e-03          0.000000e+00          1.004444e-02 
##          racepctblack          racePctWhite          racePctAsian 
##          1.862461e-01         -2.851002e-02          0.000000e+00 
##           racePctHisp           agePct12t21           agePct12t29 
##          1.351425e-02          1.224598e-02         -1.540542e-01 
##           agePct16t24            agePct65up             numbUrban 
##          0.000000e+00          0.000000e+00         -1.209772e-03 
##              pctUrban             medIncome              pctWWage 
##          3.575345e-02          0.000000e+00         -8.632610e-02 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##          2.143952e-02         -8.475446e-02          1.407931e-02 
##           pctWPubAsst            pctWRetire             medFamInc 
##          0.000000e+00         -7.184448e-02          0.000000e+00 
##             perCapInc           whitePerCap           blackPerCap 
##          0.000000e+00         -5.262569e-02         -1.843241e-02 
##          indianPerCap           AsianPerCap           OtherPerCap 
##         -2.495286e-02          2.177364e-02          3.941744e-02 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##          1.759589e-02          0.000000e+00         -1.095520e-01 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##         -2.827185e-02          0.000000e+00          0.000000e+00 
##         PctUnemployed             PctEmploy           PctEmplManu 
##         -1.750846e-02          7.310291e-02         -1.836000e-02 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##          0.000000e+00          1.346640e-03          0.000000e+00 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##          1.152398e-01          8.597559e-02         -4.552063e-02 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##          0.000000e+00          0.000000e+00          0.000000e+00 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##         -2.338313e-01         -3.680902e-02          0.000000e+00 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##          0.000000e+00         -1.093346e-01         -5.009782e-02 
##              PctIlleg              NumImmig        PctImmigRecent 
##          1.635443e-01         -9.810651e-02          0.000000e+00 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##         -1.458470e-03          0.000000e+00          0.000000e+00 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##          0.000000e+00          0.000000e+00          0.000000e+00 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##          1.348307e-02          0.000000e+00          0.000000e+00 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##         -4.570750e-03          0.000000e+00          1.220729e-02 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##          0.000000e+00          0.000000e+00         -3.577309e-02 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##          1.185666e-01          1.723298e-02          0.000000e+00 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##          1.274867e-01         -5.799251e-02          0.000000e+00 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##          4.812779e-02         -3.859766e-02         -6.629567e-03 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##          1.483078e-02         -4.728094e-05         -2.982638e-02 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##          0.000000e+00          0.000000e+00         -1.490333e-01 
##            RentMedian             RentHighQ               MedRent 
##          0.000000e+00          0.000000e+00          1.440134e-01 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##          4.775178e-02         -2.111580e-02         -6.608510e-02 
##         NumInShelters             NumStreet        PctForeignBorn 
##          6.668756e-02          1.801051e-01          3.182539e-02 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##          0.000000e+00          0.000000e+00          2.180302e-02 
##        PctSameState85              LandArea               PopDens 
##          0.000000e+00          0.000000e+00          0.000000e+00 
##        PctUsePubTrans 
##         -1.129408e-02
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(lasso.mod$lambda[60]), col="blue", lwd=4, lty=3)

datosx<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]
pred<-predict(lasso.mod,s=lasso.mod$lambda[5],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.lasso <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 0.03766304
## 
## $raiz.error.cuadratico
## [1] 0.1992323
## 
## $error.relativo
## [1] 0.6140855
## 
## $correlacion
## [1] 0.7633719
pred<-predict(lasso.mod,s=lasso.mod$lambda[60],newx=datosx)
# Medición de precisión
numero.predictoras <- dim(datosx)[2]-1
pre.lasso <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 0.01713597
## 
## $raiz.error.cuadratico
## [1] 0.1343867
## 
## $error.relativo
## [1] 0.3835721
## 
## $correlacion
## [1] 0.8272048
# Validación Cruzada
sal.cv<-cv.glmnet(x,y,alpha=1) 
plot(sal.cv)

mejor.lambda<-sal.cv$lambda.min
mejor.lambda
## [1] 0.00033764
log(mejor.lambda)
## [1] -7.99353
coef(lasso.mod)[,which(lasso.mod$lambda==mejor.lambda)]
##           (Intercept)           (Intercept)                 state 
##          0.5867939833          0.0000000000         -0.0007858794 
##                  fold            population         householdsize 
##         -0.0017100313          0.0000000000          0.0109853844 
##          racepctblack          racePctWhite          racePctAsian 
##          0.1978777359         -0.0279480711          0.0000000000 
##           racePctHisp           agePct12t21           agePct12t29 
##          0.0421725769          0.0407171466         -0.2451626702 
##           agePct16t24            agePct65up             numbUrban 
##          0.0000000000          0.0141380334         -0.0368177420 
##              pctUrban             medIncome              pctWWage 
##          0.0400617425          0.0000000000         -0.1487669827 
##          pctWFarmSelf            pctWInvInc            pctWSocSec 
##          0.0336520363         -0.1267180313          0.0414632333 
##           pctWPubAsst            pctWRetire             medFamInc 
##          0.0000000000         -0.0814707917          0.0502009501 
##             perCapInc           whitePerCap           blackPerCap 
##          0.0000000000         -0.1246292766         -0.0239358980 
##          indianPerCap           AsianPerCap           OtherPerCap 
##         -0.0276577806          0.0238124005          0.0426492109 
##            HispPerCap           NumUnderPov        PctPopUnderPov 
##          0.0249296609          0.0000000000         -0.1403779045 
##       PctLess9thGrade          PctNotHSGrad           PctBSorMore 
##         -0.0531177249          0.0000000000          0.0431119096 
##         PctUnemployed             PctEmploy           PctEmplManu 
##         -0.0123550179          0.1562679486         -0.0366996818 
##       PctEmplProfServ          PctOccupManu      PctOccupMgmtProf 
##          0.0000000000          0.0366284486          0.0162660525 
##        MalePctDivorce        MalePctNevMarr          FemalePctDiv 
##          0.1673019144          0.1520612130         -0.1188392063 
##           TotalPctDiv            PersPerFam            PctFam2Par 
##          0.0000000000          0.0000000000          0.0000000000 
##           PctKids2Par      PctYoungKids2Par           PctTeen2Par 
##         -0.2609602081         -0.0311486612          0.0000000000 
##   PctWorkMomYoungKids            PctWorkMom              NumIlleg 
##          0.0175615502         -0.1457818573         -0.0727498912 
##              PctIlleg              NumImmig        PctImmigRecent 
##          0.1454842368         -0.1036392364          0.0018233948 
##          PctImmigRec5          PctImmigRec8         PctImmigRec10 
##         -0.0036430717         -0.0004765115          0.0000000000 
##        PctRecentImmig          PctRecImmig5          PctRecImmig8 
##          0.0000000000          0.0000000000          0.0203701026 
##         PctRecImmig10      PctSpeakEnglOnly   PctNotSpeakEnglWell 
##          0.0000000000          0.0000000000         -0.0564707285 
##       PctLargHouseFam     PctLargHouseOccup      PersPerOccupHous 
##         -0.0632954934         -0.0257936939          0.2207988365 
##     PersPerOwnOccHous    PersPerRentOccHous       PctPersOwnOccup 
##         -0.0750911584         -0.0478297447         -0.0904661539 
##      PctPersDenseHous        PctHousLess3BR              MedNumBR 
##          0.1583895153          0.0478034348          0.0073570029 
##            HousVacant          PctHousOccup         PctHousOwnOcc 
##          0.1571483999         -0.0542942644          0.0000000000 
##      PctVacantBoarded        PctVacMore6Mos        MedYrHousBuilt 
##          0.0533726447         -0.0535339044         -0.0099512256 
##        PctHousNoPhone        PctWOFullPlumb        OwnOccLowQuart 
##          0.0223358029         -0.0065138077         -0.0721842596 
##          OwnOccMedVal         OwnOccHiQuart              RentLowQ 
##          0.0000000000          0.0000000000         -0.1974997576 
##            RentMedian             RentHighQ               MedRent 
##          0.0000000000          0.0000000000          0.2137257248 
##     MedRentPctHousInc      MedOwnCostPctInc MedOwnCostPctIncNoMtg 
##          0.0470860508         -0.0316079709         -0.0727961461 
##         NumInShelters             NumStreet        PctForeignBorn 
##          0.0954364773          0.1831881470          0.0627223555 
##      PctBornSameState        PctSameHouse85         PctSameCity85 
##          0.0000000000          0.0000000000          0.0261728386 
##        PctSameState85              LandArea               PopDens 
##          0.0067659166          0.0128662561          0.0000000000 
##        PctUsePubTrans 
##         -0.0262528417
plot(lasso.mod,"lambda", label=TRUE)
abline(v = log(mejor.lambda), col="blue", lwd=4, lty=3)

pred<-predict(lasso.mod,s=mejor.lambda,newx=datosx)
# Medición de precisión
numero.predictoras<- dim(datosx)[2]-1
numero.predictoras
## [1] 101
pre.lasso <- indices.precision(datos$ViolentCrimesPerPop,pred,numero.predictoras)
pre.lasso
## $error.cuadratico
## [1] 0.01676862
## 
## $raiz.error.cuadratico
## [1] 0.1329385
## 
## $error.relativo
## [1] 0.3807958
## 
## $correlacion
## [1] 0.8312561

###Elastic Net

# Debemos eliminar la columna 1
x<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]
head(x)
##   (Intercept) state fold population householdsize racepctblack racePctWhite
## 1           1     8    1       0.19          0.33         0.02         0.90
## 2           1    53    1       0.00          0.16         0.12         0.74
## 3           1    24    1       0.00          0.42         0.49         0.56
## 4           1    34    1       0.04          0.77         1.00         0.08
## 5           1    42    1       0.01          0.55         0.02         0.95
## 6           1     6    1       0.02          0.28         0.06         0.54
##   racePctAsian racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up
## 1         0.12        0.17        0.34        0.47        0.29       0.32
## 2         0.45        0.07        0.26        0.59        0.35       0.27
## 3         0.17        0.04        0.39        0.47        0.28       0.32
## 4         0.12        0.10        0.51        0.50        0.34       0.21
## 5         0.09        0.05        0.38        0.38        0.23       0.36
## 6         1.00        0.25        0.31        0.48        0.27       0.37
##   numbUrban pctUrban medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec
## 1      0.20      1.0      0.37     0.72         0.34       0.60       0.29
## 2      0.02      1.0      0.31     0.72         0.11       0.45       0.25
## 3      0.00      0.0      0.30     0.58         0.19       0.39       0.38
## 4      0.06      1.0      0.58     0.89         0.21       0.43       0.36
## 5      0.02      0.9      0.50     0.72         0.16       0.68       0.44
## 6      0.04      1.0      0.52     0.68         0.20       0.61       0.28
##   pctWPubAsst pctWRetire medFamInc perCapInc whitePerCap blackPerCap
## 1        0.15       0.43      0.39      0.40        0.39        0.32
## 2        0.29       0.39      0.29      0.37        0.38        0.33
## 3        0.40       0.84      0.28      0.27        0.29        0.27
## 4        0.20       0.82      0.51      0.36        0.40        0.39
## 5        0.11       0.71      0.46      0.43        0.41        0.28
## 6        0.15       0.25      0.62      0.72        0.76        0.77
##   indianPerCap AsianPerCap OtherPerCap HispPerCap NumUnderPov PctPopUnderPov
## 1         0.27        0.27        0.36       0.41        0.08           0.19
## 2         0.16        0.30        0.22       0.35        0.01           0.24
## 3         0.07        0.29        0.28       0.39        0.01           0.27
## 4         0.16        0.25        0.36       0.44        0.01           0.10
## 5         0.00        0.74        0.51       0.48        0.00           0.06
## 6         0.28        0.52        0.48       0.60        0.01           0.12
##   PctLess9thGrade PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu
## 1            0.10         0.18        0.48          0.27      0.68        0.23
## 2            0.14         0.24        0.30          0.27      0.73        0.57
## 3            0.27         0.43        0.19          0.36      0.58        0.32
## 4            0.09         0.25        0.31          0.33      0.71        0.36
## 5            0.25         0.30        0.33          0.12      0.65        0.67
## 6            0.13         0.12        0.80          0.10      0.65        0.19
##   PctEmplProfServ PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr
## 1            0.41         0.25             0.52           0.68           0.40
## 2            0.15         0.42             0.36           1.00           0.63
## 3            0.29         0.49             0.32           0.63           0.41
## 4            0.45         0.37             0.39           0.34           0.45
## 5            0.38         0.42             0.46           0.22           0.27
## 6            0.77         0.06             0.91           0.49           0.57
##   FemalePctDiv TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par
## 1         0.75        0.75       0.35       0.55        0.59             0.61
## 2         0.91        1.00       0.29       0.43        0.47             0.60
## 3         0.71        0.70       0.45       0.42        0.44             0.43
## 4         0.49        0.44       0.75       0.65        0.54             0.83
## 5         0.20        0.21       0.51       0.91        0.91             0.89
## 6         0.61        0.58       0.44       0.62        0.69             0.87
##   PctTeen2Par PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig
## 1        0.56                0.74       0.76     0.04     0.14     0.03
## 2        0.39                0.46       0.53     0.00     0.24     0.01
## 3        0.43                0.71       0.67     0.01     0.46     0.00
## 4        0.65                0.85       0.86     0.03     0.33     0.02
## 5        0.85                0.40       0.60     0.00     0.06     0.00
## 6        0.53                0.30       0.43     0.00     0.11     0.04
##   PctImmigRecent PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig
## 1           0.24         0.27         0.37          0.39           0.07
## 2           0.52         0.62         0.64          0.63           0.25
## 3           0.07         0.06         0.15          0.19           0.02
## 4           0.11         0.20         0.30          0.31           0.05
## 5           0.03         0.07         0.20          0.27           0.01
## 6           0.30         0.35         0.43          0.47           0.50
##   PctRecImmig5 PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## 1         0.07         0.08          0.08             0.89                0.06
## 2         0.27         0.25          0.23             0.84                0.10
## 3         0.02         0.04          0.05             0.88                0.04
## 4         0.08         0.11          0.11             0.81                0.08
## 5         0.02         0.04          0.05             0.88                0.05
## 6         0.50         0.56          0.57             0.45                0.28
##   PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## 1            0.14              0.13             0.33              0.39
## 2            0.16              0.10             0.17              0.29
## 3            0.20              0.20             0.46              0.52
## 4            0.56              0.62             0.85              0.77
## 5            0.16              0.19             0.59              0.60
## 6            0.25              0.19             0.29              0.53
##   PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
## 1               0.28            0.55             0.09           0.51      0.5
## 2               0.17            0.26             0.20           0.82      0.0
## 3               0.43            0.42             0.15           0.51      0.5
## 4               1.00            0.94             0.12           0.01      0.5
## 5               0.37            0.89             0.02           0.19      0.5
## 6               0.18            0.39             0.26           0.73      0.0
##   HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
## 1       0.21         0.71          0.52             0.05           0.26
## 2       0.02         0.79          0.24             0.02           0.25
## 3       0.01         0.86          0.41             0.29           0.30
## 4       0.01         0.97          0.96             0.60           0.47
## 5       0.01         0.89          0.87             0.04           0.55
## 6       0.02         0.84          0.30             0.16           0.28
##   MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
## 1           0.65           0.14           0.06           0.22         0.19
## 2           0.65           0.16           0.00           0.21         0.20
## 3           0.52           0.47           0.45           0.18         0.17
## 4           0.52           0.11           0.11           0.24         0.21
## 5           0.73           0.05           0.14           0.31         0.31
## 6           0.25           0.02           0.05           0.94         1.00
##   OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
## 1          0.18     0.36       0.35      0.38    0.34              0.38
## 2          0.21     0.42       0.38      0.40    0.37              0.29
## 3          0.16     0.27       0.29      0.27    0.31              0.48
## 4          0.19     0.75       0.70      0.77    0.89              0.63
## 5          0.30     0.40       0.36      0.38    0.38              0.22
## 6          1.00     0.67       0.63      0.68    0.62              0.47
##   MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
## 1             0.46                  0.25          0.04         0           0.12
## 2             0.32                  0.18          0.00         0           0.21
## 3             0.39                  0.28          0.00         0           0.14
## 4             0.51                  0.47          0.00         0           0.19
## 5             0.51                  0.21          0.00         0           0.11
## 6             0.59                  0.11          0.00         0           0.70
##   PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
## 1             0.42           0.50          0.51           0.64     0.12    0.26
## 2             0.50           0.34          0.60           0.52     0.02    0.12
## 3             0.49           0.54          0.67           0.56     0.01    0.21
## 4             0.30           0.73          0.64           0.65     0.02    0.39
## 5             0.72           0.64          0.61           0.53     0.04    0.09
## 6             0.42           0.49          0.73           0.64     0.01    0.58
##   PctUsePubTrans
## 1           0.20
## 2           0.45
## 3           0.02
## 4           0.28
## 5           0.02
## 6           0.10
# La siguiente instrucción construye la variable a predecir
y<-datos$ViolentCrimesPerPop
library(glmnet)
datosx<-model.matrix(ViolentCrimesPerPop~.,datos)[,-c(103)]

v.alpha<-c(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1)

for(ss in v.alpha) {
  cat("========ELASTIC NET========\n")
  cat("Alpha=",ss,"\n")
  net.mod<-glmnet(x,y,alpha=ss)
  sal.cv<-cv.glmnet(x,y,alpha=ss) 
  mejor.lambda<-sal.cv$lambda.min
  pred<-predict(net.mod,s=mejor.lambda,newx=datosx)
  res<-MSE(pred,datos$ViolentCrimesPerPop)
  cat("MSE",res,"\n")
}
## ========ELASTIC NET========
## Alpha= 0 
## MSE 0.01720385 
## ========ELASTIC NET========
## Alpha= 0.1 
## MSE 0.01669266 
## ========ELASTIC NET========
## Alpha= 0.2 
## MSE 0.01694991 
## ========ELASTIC NET========
## Alpha= 0.3 
## MSE 0.01674577 
## ========ELASTIC NET========
## Alpha= 0.4 
## MSE 0.01680713 
## ========ELASTIC NET========
## Alpha= 0.5 
## MSE 0.01682668 
## ========ELASTIC NET========
## Alpha= 0.6 
## MSE 0.01675929 
## ========ELASTIC NET========
## Alpha= 0.7 
## MSE 0.01672877 
## ========ELASTIC NET========
## Alpha= 0.8 
## MSE 0.01672512 
## ========ELASTIC NET========
## Alpha= 0.9 
## MSE 0.0167222 
## ========ELASTIC NET========
## Alpha= 1 
## MSE 0.0167005
  1. ¿Cu´al m´etodo usar´ıa con base en la informaci´on obtenida en los ejercicios anteriores?

En este caso el error cuadratico medio y error relativo mas bajo parece ser nuevamente la regresion de Lasso, la diferencia resulta no muy grande, pero mejor.

Pregunta 4: 1. Programe en R una funci´on lm2(…) que recibe como par´ametro una tabla de aprendizaje y retorna un modelo de Regresi´on Lineal, es decir, calcula y retorna β = (XtX) −1Xt y.

x<-model.matrix(ViolentCrimesPerPop~.,taprendizaje)[,-c(103)]
y<-datos$ViolentCrimesPerPop

lm2<-function(X,Y){
  n<-nrow(X)
  k<-ncol(X)
  Constante<-seq(1,1,length.out =n)
  
  datos1<-data.frame(Constante,X)
  
  X<-as.matrix(datos1)
  Y<-as.matrix(Y)

  XtX<-t(X)%*%X
  XtX.inv<-solve(XtX)
  XY<-t(X)%*%Y

  Betas<-solve(t(X)%*%X)%*%(t(X)%*%Y)

  B<-data.frame(Coeficientes=Betas)
  B
  }
lm2(datos[,1:102], datos[,103])
##                        Coeficientes
## Constante              0.5987695284
## state                 -0.0007436828
## fold                  -0.0015747954
## population             0.1321909463
## householdsize          0.0007521874
## racepctblack           0.2000845595
## racePctWhite          -0.0545392566
## racePctAsian          -0.0132277730
## racePctHisp            0.0537790097
## agePct12t21            0.1156000167
## agePct12t29           -0.2374790232
## agePct16t24           -0.1330132267
## agePct65up             0.0364240293
## numbUrban             -0.2461313866
## pctUrban               0.0468132605
## medIncome             -0.1778504926
## pctWWage              -0.1977078909
## pctWFarmSelf           0.0466592440
## pctWInvInc            -0.1600134323
## pctWSocSec             0.0829735051
## pctWPubAsst           -0.0064110991
## pctWRetire            -0.0862223022
## medFamInc              0.2783260665
## perCapInc              0.1090832142
## whitePerCap           -0.3540081300
## blackPerCap           -0.0322051945
## indianPerCap          -0.0332265789
## AsianPerCap            0.0198696225
## OtherPerCap            0.0446076633
## HispPerCap             0.0312483584
## NumUnderPov            0.1257067673
## PctPopUnderPov        -0.1723615272
## PctLess9thGrade       -0.1019432432
## PctNotHSGrad           0.0529293524
## PctBSorMore            0.0548988639
## PctUnemployed          0.0024202568
## PctEmploy              0.2636936643
## PctEmplManu           -0.0611584964
## PctEmplProfServ       -0.0231200271
## PctOccupManu           0.0725968767
## PctOccupMgmtProf       0.1095694309
## MalePctDivorce         0.4315918094
## MalePctNevMarr         0.2211649847
## FemalePctDiv           0.1139415282
## TotalPctDiv           -0.4977382607
## PersPerFam            -0.1600699304
## PctFam2Par            -0.0143193239
## PctKids2Par           -0.2870569326
## PctYoungKids2Par      -0.0267124056
## PctTeen2Par           -0.0025638736
## PctWorkMomYoungKids    0.0523100472
## PctWorkMom            -0.1888669617
## NumIlleg              -0.1383370705
## PctIlleg               0.1143173345
## NumImmig              -0.1403383194
## PctImmigRecent         0.0221217598
## PctImmigRec5           0.0242018090
## PctImmigRec8          -0.0690847068
## PctImmigRec10          0.0360764526
## PctRecentImmig        -0.0244705538
## PctRecImmig5          -0.2037795787
## PctRecImmig8           0.3916333568
## PctRecImmig10         -0.1607643949
## PctSpeakEnglOnly      -0.0265884653
## PctNotSpeakEnglWell   -0.1367050018
## PctLargHouseFam        0.0572615051
## PctLargHouseOccup     -0.1874714841
## PersPerOccupHous       0.5663796516
## PersPerOwnOccHous     -0.0452152549
## PersPerRentOccHous    -0.2410336539
## PctPersOwnOccup       -0.6954981880
## PctPersDenseHous       0.2086599671
## PctHousLess3BR         0.0849001959
## MedNumBR               0.0265379417
## HousVacant             0.1541798393
## PctHousOccup          -0.0495126580
## PctHousOwnOcc          0.5636854419
## PctVacantBoarded       0.0543938209
## PctVacMore6Mos        -0.0717601786
## MedYrHousBuilt        -0.0231801967
## PctHousNoPhone         0.0189780846
## PctWOFullPlumb        -0.0139302138
## OwnOccLowQuart        -0.3956505825
## OwnOccMedVal           0.2677203447
## OwnOccHiQuart          0.0194529573
## RentLowQ              -0.2313898655
## RentMedian            -0.0012952529
## RentHighQ             -0.0571824169
## MedRent                0.3417753478
## MedRentPctHousInc      0.0424070175
## MedOwnCostPctInc      -0.0404555487
## MedOwnCostPctIncNoMtg -0.0739524561
## NumInShelters          0.1343449602
## NumStreet              0.1754496414
## PctForeignBorn         0.1145704673
## PctBornSameState       0.0166478623
## PctSameHouse85        -0.0038043813
## PctSameCity85          0.0190145485
## PctSameState85         0.0134812735
## LandArea               0.0176207904
## PopDens               -0.0113546651
## PctUsePubTrans        -0.0370162514
## LemasPctOfficDrugUn    0.0244668241
  1. Programe en R una funci´on predict2(…) que recibe como par´ametro el modelo construido en la pregunta anterior, una tabla de testing de modo tal que retorna la predicci´on para esta tabla de testing.
# numero.predictoras <- dim(datos)[2] - 1
# # Hace la Predicción
# prediccion <- predict(modelo.lm, ttesting)
# # Medición de precisión
# pre.lm <- indices.precision(ttesting$ViolentCrimesPerPop, prediccion,numero.predictoras)
# pre.lm

x<-model.matrix(ViolentCrimesPerPop~.,ttesting)[,-c(103)]
y<-datos$ViolentCrimesPerPop


predict2<-function(x,y){
  n<-nrow(x)
  k<-ncol(x)
  Constante<-seq(1,1,length.out =n)
  
  datos1<-data.frame(Constante,x)
  
  X<-as.matrix(datos1)
  Y<-as.matrix(y)

  XtX<-t(X)%*%X
  XtX.inv<-solve(XtX)
  XY<-t(X)%*%Y

  Betas<-solve(t(X)%*%X)%*%(t(X)%*%Y)

    # Suma de Cuadrados
  
  Syy<-t(Y)%*%Y-nrow(Y)*mean(Y)^2
  SSE=t(Y)%*%Y - t(Betas)%*%XY
  SSR<-t(Betas)%*%XY-nrow(Y)*mean(Y)^2
  
  # Grados de libertad
  gl1=k
  gl2=nrow(Y)-(k+1)
  
  # test para Betas
  MSE=SSE/gl2
  Varianzas<-as.vector(MSE)*diag(XtX.inv)
  Desviaciones<-sqrt(Varianzas)
  diagonal<-diag(Desviaciones,k+1,k+1)

    t<-t(t(Betas)%*%solve(diagonal))
  
    v.p<-2*pt(abs(t),gl2,lower.tail=FALSE)

  estimaciones<-data.frame(Coeficientes=Betas,t_student = t,Valor_p=v.p)
  estimaciones
  }

predict2(datos[,1:102], datos[,103])
##                        Coeficientes    t_student      Valor_p
## Constante              0.5987695284  2.948030360 3.237254e-03
## state                 -0.0007436828 -2.976976198 2.948044e-03
## fold                  -0.0015747954 -1.493702715 1.354202e-01
## population             0.1321909463  0.333126648 7.390756e-01
## householdsize          0.0007521874  0.008695378 9.930631e-01
## racepctblack           0.2000845595  3.916548999 9.301525e-05
## racePctWhite          -0.0545392566 -0.928818670 3.531016e-01
## racePctAsian          -0.0132277730 -0.385534897 6.998845e-01
## racePctHisp            0.0537790097  1.007286857 3.139258e-01
## agePct12t21            0.1156000167  1.093397074 2.743588e-01
## agePct12t29           -0.2374790232 -1.519992547 1.286800e-01
## agePct16t24           -0.1330132267 -0.811150728 4.173812e-01
## agePct65up             0.0364240293  0.352318077 7.246390e-01
## numbUrban             -0.2461313866 -0.636496140 5.245301e-01
## pctUrban               0.0468132605  2.998678423 2.746906e-03
## medIncome             -0.1778504926 -1.031315916 3.025246e-01
## pctWWage              -0.1977078909 -2.210510911 2.718905e-02
## pctWFarmSelf           0.0466592440  2.319567189 2.047034e-02
## pctWInvInc            -0.1600134323 -2.363496873 1.820402e-02
## pctWSocSec             0.0829735051  0.775991294 4.378512e-01
## pctWPubAsst           -0.0064110991 -0.138978280 8.894821e-01
## pctWRetire            -0.0862223022 -2.344545091 1.915327e-02
## medFamInc              0.2783260665  1.737868691 8.239674e-02
## perCapInc              0.1090832142  0.578816542 5.627819e-01
## whitePerCap           -0.3540081300 -2.325004146 2.017711e-02
## blackPerCap           -0.0322051945 -1.266305516 2.055597e-01
## indianPerCap          -0.0332265789 -1.713602469 8.676565e-02
## AsianPerCap            0.0198696225  1.051247432 2.932793e-01
## OtherPerCap            0.0446076633  2.387168751 1.707643e-02
## HispPerCap             0.0312483584  1.258424233 2.083937e-01
## NumUnderPov            0.1257067673  0.911880007 3.619481e-01
## PctPopUnderPov        -0.1723615272 -2.749185557 6.031177e-03
## PctLess9thGrade       -0.1019432432 -1.504976042 1.324972e-01
## PctNotHSGrad           0.0529293524  0.552540650 5.806433e-01
## PctBSorMore            0.0548988639  0.710047773 4.777621e-01
## PctUnemployed          0.0024202568  0.059471293 9.525830e-01
## PctEmploy              0.2636936643  3.340902695 8.513155e-04
## PctEmplManu           -0.0611584964 -1.908903265 5.642581e-02
## PctEmplProfServ       -0.0231200271 -0.566713457 5.709761e-01
## PctOccupManu           0.0725968767  1.322024223 1.863200e-01
## PctOccupMgmtProf       0.1095694309  1.270554684 2.040434e-01
## MalePctDivorce         0.4315918094  1.744659031 8.120669e-02
## MalePctNevMarr         0.2211649847  3.257799723 1.142688e-03
## FemalePctDiv           0.1139415282  0.368541220 7.125110e-01
## TotalPctDiv           -0.4977382607 -0.961061627 3.366441e-01
## PersPerFam            -0.1600699304 -0.950913762 3.417697e-01
## PctFam2Par            -0.0143193239 -0.089656207 9.285699e-01
## PctKids2Par           -0.2870569326 -1.845595995 6.510712e-02
## PctYoungKids2Par      -0.0267124056 -0.554285571 5.794490e-01
## PctTeen2Par           -0.0025638736 -0.060208601 9.519959e-01
## PctWorkMomYoungKids    0.0523100472  1.113225238 2.657532e-01
## PctWorkMom            -0.1888669617 -3.512753258 4.539057e-04
## NumIlleg              -0.1383370705 -1.276631412 2.018892e-01
## PctIlleg               0.1143173345  2.408390497 1.611817e-02
## NumImmig              -0.1403383194 -1.801761634 7.174213e-02
## PctImmigRecent         0.0221217598  0.539503302 5.896031e-01
## PctImmigRec5           0.0242018090  0.363968668 7.159221e-01
## PctImmigRec8          -0.0690847068 -0.896133284 3.702956e-01
## PctImmigRec10          0.0360764526  0.605350965 5.450185e-01
## PctRecentImmig        -0.0244705538 -0.200481076 8.411259e-01
## PctRecImmig5          -0.2037795787 -0.921567036 3.568720e-01
## PctRecImmig8           0.3916333568  1.433677615 1.518296e-01
## PctRecImmig10         -0.1607643949 -0.734613973 4.626657e-01
## PctSpeakEnglOnly      -0.0265884653 -0.378231555 7.053010e-01
## PctNotSpeakEnglWell   -0.1367050018 -1.998406580 4.581555e-02
## PctLargHouseFam        0.0572615051  0.253556604 7.998657e-01
## PctLargHouseOccup     -0.1874714841 -0.793054630 4.278455e-01
## PersPerOccupHous       0.5663796516  2.262691841 2.376770e-02
## PersPerOwnOccHous     -0.0452152549 -0.269618833 7.874829e-01
## PersPerRentOccHous    -0.2410336539 -2.979933794 2.919863e-03
## PctPersOwnOccup       -0.6954981880 -1.944430238 5.199170e-02
## PctPersDenseHous       0.2086599671  2.761121296 5.815978e-03
## PctHousLess3BR         0.0849001959  1.442889382 1.492173e-01
## MedNumBR               0.0265379417  1.365662098 1.722075e-01
## HousVacant             0.1541798393  2.113030849 3.472884e-02
## PctHousOccup          -0.0495126580 -1.600882000 1.095701e-01
## PctHousOwnOcc          0.5636854419  1.507093326 1.319538e-01
## PctVacantBoarded       0.0543938209  2.541694184 1.111089e-02
## PctVacMore6Mos        -0.0717601786 -2.853817056 4.366775e-03
## MedYrHousBuilt        -0.0231801967 -0.801567303 4.229040e-01
## PctHousNoPhone         0.0189780846  0.538337996 5.904071e-01
## PctWOFullPlumb        -0.0139302138 -0.688423152 4.912708e-01
## OwnOccLowQuart        -0.3956505825 -1.934866517 5.315565e-02
## OwnOccMedVal           0.2677203447  0.872314314 3.831477e-01
## OwnOccHiQuart          0.0194529573  0.118193677 9.059268e-01
## RentLowQ              -0.2313898655 -3.454729518 5.629978e-04
## RentMedian            -0.0012952529 -0.008274622 9.933988e-01
## RentHighQ             -0.0571824169 -0.663902822 5.068334e-01
## MedRent                0.3417753478  2.636147114 8.453922e-03
## MedRentPctHousInc      0.0424070175  1.303088855 1.927031e-01
## MedOwnCostPctInc      -0.0404555487 -1.173834915 2.406090e-01
## MedOwnCostPctIncNoMtg -0.0739524561 -3.002945955 2.708862e-03
## NumInShelters          0.1343449602  2.095238847 3.628247e-02
## NumStreet              0.1754496414  3.725871871 2.003541e-04
## PctForeignBorn         0.1145704673  1.275673208 2.022277e-01
## PctBornSameState       0.0166478623  0.399058443 6.898952e-01
## PctSameHouse85        -0.0038043813 -0.065865085 9.474922e-01
## PctSameCity85          0.0190145485  0.499023424 6.178209e-01
## PctSameState85         0.0134812735  0.316203670 7.518828e-01
## LandArea               0.0176207904  0.359101486 7.195593e-01
## PopDens               -0.0113546651 -0.374491636 7.080806e-01
## PctUsePubTrans        -0.0370162514 -1.600471863 1.096610e-01
## LemasPctOfficDrugUn    0.0244668241  1.584719873 1.131972e-01
  1. Usando la tabla de datos uscrime.csv compare los resultados de las funciones lm(…), lm2(…), predict(…) y predict2(…).
lm <- lm(ViolentCrimesPerPop~., data = taprendizaje)
lm
## 
## Call:
## lm(formula = ViolentCrimesPerPop ~ ., data = taprendizaje)
## 
## Coefficients:
##           (Intercept)                  state                   fold  
##             0.7301321             -0.0005933             -0.0017368  
##            population          householdsize           racepctblack  
##            -0.0269510              0.1004843              0.1236990  
##          racePctWhite           racePctAsian            racePctHisp  
##            -0.1437003             -0.0136794              0.0632988  
##           agePct12t21            agePct12t29            agePct16t24  
##             0.1643552             -0.1815100             -0.1828198  
##            agePct65up              numbUrban               pctUrban  
##             0.0195642             -0.1106344              0.0441169  
##             medIncome               pctWWage           pctWFarmSelf  
##            -0.2885981             -0.1594796              0.0302056  
##            pctWInvInc             pctWSocSec            pctWPubAsst  
##            -0.1186551              0.0885469             -0.0189197  
##            pctWRetire              medFamInc              perCapInc  
##            -0.0795092              0.3339612              0.1833208  
##           whitePerCap            blackPerCap           indianPerCap  
##            -0.4143148             -0.0432160             -0.0329392  
##           AsianPerCap            OtherPerCap             HispPerCap  
##             0.0050798              0.0291808              0.0579221  
##           NumUnderPov         PctPopUnderPov        PctLess9thGrade  
##             0.1147659             -0.1817281             -0.0572288  
##          PctNotHSGrad            PctBSorMore          PctUnemployed  
##             0.0424882              0.0315698              0.0096606  
##             PctEmploy            PctEmplManu        PctEmplProfServ  
##             0.2326172             -0.0813150             -0.0113773  
##          PctOccupManu       PctOccupMgmtProf         MalePctDivorce  
##             0.0817852              0.0899506              0.3950949  
##        MalePctNevMarr           FemalePctDiv            TotalPctDiv  
##             0.0998573              0.0525902             -0.4152243  
##            PersPerFam             PctFam2Par            PctKids2Par  
##            -0.2326905             -0.1843596             -0.1693039  
##      PctYoungKids2Par            PctTeen2Par    PctWorkMomYoungKids  
##            -0.0130657             -0.0151195              0.0737985  
##            PctWorkMom               NumIlleg               PctIlleg  
##            -0.2160931             -0.2395280              0.1607720  
##              NumImmig         PctImmigRecent           PctImmigRec5  
##            -0.1207343              0.0516954              0.0117994  
##          PctImmigRec8          PctImmigRec10         PctRecentImmig  
##            -0.0904042              0.0762501             -0.0206852  
##          PctRecImmig5           PctRecImmig8          PctRecImmig10  
##            -0.4302941              0.5926712             -0.0721819  
##      PctSpeakEnglOnly    PctNotSpeakEnglWell        PctLargHouseFam  
##            -0.0643134             -0.1564434              0.1642796  
##     PctLargHouseOccup       PersPerOccupHous      PersPerOwnOccHous  
##            -0.2961117              0.5282477              0.0516878  
##    PersPerRentOccHous        PctPersOwnOccup       PctPersDenseHous  
##            -0.3074837             -0.9441568              0.1575127  
##        PctHousLess3BR               MedNumBR             HousVacant  
##             0.0887776              0.0264781              0.2230062  
##          PctHousOccup          PctHousOwnOcc       PctVacantBoarded  
##            -0.0416508              0.7927967              0.0336534  
##        PctVacMore6Mos         MedYrHousBuilt         PctHousNoPhone  
##            -0.0716178             -0.0122728             -0.0202421  
##        PctWOFullPlumb         OwnOccLowQuart           OwnOccMedVal  
##            -0.0210938             -0.1794044              0.0972415  
##         OwnOccHiQuart               RentLowQ             RentMedian  
##             0.0467856             -0.2278201             -0.1073302  
##             RentHighQ                MedRent      MedRentPctHousInc  
##             0.0083983              0.3802668              0.0431364  
##      MedOwnCostPctInc  MedOwnCostPctIncNoMtg          NumInShelters  
##            -0.0552143             -0.0512172              0.2211884  
##             NumStreet         PctForeignBorn       PctBornSameState  
##             0.1516897              0.0156050             -0.0006032  
##        PctSameHouse85          PctSameCity85         PctSameState85  
##             0.0454816              0.0269999              0.0097497  
##              LandArea                PopDens         PctUsePubTrans  
##             0.0064374             -0.0441963             -0.0510265  
##   LemasPctOfficDrugUn  
##             0.0251963
prediccion <- predict(lm, ttesting)
head(prediccion)
##        215       1798        723        892        314        998 
## 0.07968485 0.12377464 0.23335915 0.09209358 0.42702317 0.15911575
  1. Usando la tabla de datos uscrime.csv y la funci´on de R denominada system.time(…) compare los tiempos de ejecuci´on de las funciones lm(…), lm2(…), predict(…) y predict2(…).
system.time(lm(ViolentCrimesPerPop~., data = taprendizaje))
##    user  system elapsed 
##    0.02    0.00    0.03
system.time(predict(lm, ttesting))
##    user  system elapsed 
##       0       0       0
system.time(lm2(datos[,1:102], datos[,103]))
##    user  system elapsed 
##    0.03    0.00    0.03
system.time(predict2(datos[,1:102], datos[,103]))
##    user  system elapsed 
##    0.03    0.00    0.03

Es mas rapido lm y predict.

Pregunta 5: Demuestre que la Regresi´on Ridge puede ser obtenida mediante Regresi´on Lineal cl´asica usando una versi´on aumentada de la tabla de datos de la siguiente manera: Se aumenta la tabla de datos X con p filas adicionales √λI; y se aumenta y con p ceros, es decir: Xe =

##Ver arhcivo adjunto

Pregunta 6: (a) Supongamos que ejecutamos una regresi´on Ridge con par´ametro λ en una sola variable X, y se obtiene el coeficiente a. Ahora incluimos una copia exacta X? = X y volvemos a calcular la regresi´on Ridge. Demuestre que ambos coeficientes son id´enticos y calcule su valor. Demuestre en general que si m copias de la variable Xj son incluidas en la regresi´on Ridge, entonces sus coeficientes son todos iguales. Sugerencia: Considere matrices como las siguientes: X =

  1. ¿Qu´e pasa en Regresi´on Lasso? ¿Ocurre lo mismo?

##Ver arhcivo adjunto

Pregunta 7: En este ejercicio usaremos los datos (voces.csv). Se trata de un problema de reconocimiento de g´enero mediante el an´alisis de la voz y el habla. Esta base de datos fue creada para identificar una voz como masculina o femenina, bas´andose en las propiedades ac´usticas de la voz y el habla. El conjunto de datos consta de 3.168 muestras de voz grabadas, recogidas de hablantes masculinos y femeninos. Las muestras de voz se preprocesan mediante an´alisis

  1. Cargue la tabla de datos voces.csv en R. No olvide recodificar la variable a predecir como categ´orica.
setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase3")
datos<-read.csv("voces.csv",dec='.',header=T)
str(datos)
## 'data.frame':    3168 obs. of  21 variables:
##  $ meanfreq: num  0.0598 0.066 0.0773 0.1512 0.1351 ...
##  $ sd      : num  0.0642 0.0673 0.0838 0.0721 0.0791 ...
##  $ median  : num  0.032 0.0402 0.0367 0.158 0.1247 ...
##  $ Q25     : num  0.0151 0.0194 0.0087 0.0966 0.0787 ...
##  $ Q75     : num  0.0902 0.0927 0.1319 0.208 0.206 ...
##  $ IQR     : num  0.0751 0.0733 0.1232 0.1114 0.1273 ...
##  $ skew    : num  12.86 22.42 30.76 1.23 1.1 ...
##  $ kurt    : num  274.4 634.61 1024.93 4.18 4.33 ...
##  $ sp.ent  : num  0.893 0.892 0.846 0.963 0.972 ...
##  $ sfm     : num  0.492 0.514 0.479 0.727 0.784 ...
##  $ mode    : num  0 0 0 0.0839 0.1043 ...
##  $ centroid: num  0.0598 0.066 0.0773 0.1512 0.1351 ...
##  $ meanfun : num  0.0843 0.1079 0.0987 0.089 0.1064 ...
##  $ minfun  : num  0.0157 0.0158 0.0157 0.0178 0.0169 ...
##  $ maxfun  : num  0.276 0.25 0.271 0.25 0.267 ...
##  $ meandom : num  0.00781 0.00901 0.00799 0.2015 0.71281 ...
##  $ mindom  : num  0.00781 0.00781 0.00781 0.00781 0.00781 ...
##  $ maxdom  : num  0.00781 0.05469 0.01562 0.5625 5.48438 ...
##  $ dfrange : num  0 0.04688 0.00781 0.55469 5.47656 ...
##  $ modindx : num  0 0.0526 0.0465 0.2471 0.2083 ...
##  $ genero  : Factor w/ 2 levels "Femenino","Masculino": 2 2 2 2 2 2 2 2 2 2 ...
datos$genero <- factor(datos$genero,ordered = TRUE) ##ya es un factor es una linea repetitiva pero que ordena 
barplot(prop.table(table(datos$genero)),col=c("orange","blue","green"),main="Distribución de la variable por predecir")

  1. Usando el comando sample de R genere al azar una tabla aprendizaje con un 80 % de los datos y con el resto de los datos genere una tabla de aprendizaje.
muestra <- sample(1:nrow(datos),floor(nrow(datos)*0.20))
ttesting <- datos[muestra,]
taprendizaje <- datos[-muestra,]
nrow(ttesting)
## [1] 633
nrow(taprendizaje)
## [1] 2535
  1. Usando el m´etodo de Regresi´on Log´ıstica (con glm y glmnet) genere modelos predictivos para la tabla de aprendizaje. Luego para estos modelos calcule la matriz de confusi´on, la precisi´on, la precisi´on positiva, la precisi´on negativa, los falsos positivos, los falsos negativos, la acertividad positiva y la acertividad negativa.
indices.general <- function(MC) {
  precision.global <- sum(diag(MC))/sum(MC)
  error.global <- 1 - precision.global
  precision.categoria <- diag(MC)/rowSums(MC)
  precision.positiva <- MC[2, 2]/(MC[2, 2] + MC[2, 1])
  precision.negativa <- MC[1, 1]/(MC[1, 1] + MC[1, 2])
  falsos.positivos <- 1 - precision.negativa
  falsos.negativos <- 1 - precision.positiva
  asertividad.positiva <- MC[2, 2]/(MC[1, 2] + MC[2, 2])
  asertividad.negativa <- MC[1, 1]/(MC[1, 1] + MC[2, 1])
  res <- list(matriz.confusion = MC, precision.global = precision.global, error.global = error.global, 
              precision.categoria = precision.categoria, precision.positiva = precision.positiva, precision.negativa=precision.negativa, 
              falsos.positivos=falsos.positivos, falsos.negativos=falsos.negativos, asertividad.positiva=asertividad.positiva,
              asertividad.negativa=asertividad.negativa)
  names(res) <- c("Matriz de Confusión", "Precisión Global", "Error Global", "Precisión por categoría", "Precision Positiva", "Precision Negativa",
                  "Falsos Positivos", "Falsos Negativos", "Asertividad Positiva", "Asertividad Negativa")
  return(res)
}
library(corrplot)
library(glmnet)
library(dygraphs)
library(tidyverse)
modelo <- glm(genero ~ . , data = taprendizaje, family = binomial)
probabilidades <- predict(modelo, ttesting, type = "response")
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type == :
## prediction from a rank-deficient fit may be misleading
head(probabilidades)
##          684         2667         2396         1259         2691         1833 
## 9.970566e-01 7.745460e-01 3.507848e-05 7.807961e-01 8.550824e-02 6.146585e-04
prediccion <- rep("No", dim(ttesting)[1])
prediccion[probabilidades > 0.5] = "Si"  # Porque 1=Si entonces P>=0.5 => Si
Actual <- ttesting$genero
## Matriz de Confusión
MC <- table(Actual, prediccion)
indices.general(MC)
## $`Matriz de Confusión`
##            prediccion
## Actual       No  Si
##   Femenino  323  12
##   Masculino   4 294
## 
## $`Precisión Global`
## [1] 0.9747235
## 
## $`Error Global`
## [1] 0.02527646
## 
## $`Precisión por categoría`
##  Femenino Masculino 
## 0.9641791 0.9865772 
## 
## $`Precision Positiva`
## [1] 0.9865772
## 
## $`Precision Negativa`
## [1] 0.9641791
## 
## $`Falsos Positivos`
## [1] 0.0358209
## 
## $`Falsos Negativos`
## [1] 0.01342282
## 
## $`Asertividad Positiva`
## [1] 0.9607843
## 
## $`Asertividad Negativa`
## [1] 0.9877676
  1. Construya una tabla para los ´ındices anteriores que permita comparar el resultados de los m´etodos Regresi´on Log´ıstica Cl´asica, Ridge y Lasso ¿Cu´al m´etodo es mejor? Campare tambi´en con los resultados de las tareas anteriores. ¿Cu´al m´etodo es mejor?

#LASSO

x <- model.matrix(genero ~ ., taprendizaje)[,-1]
head(x)
##     meanfreq         sd     median         Q25        Q75        IQR      skew
## 1 0.05978098 0.06424127 0.03202691 0.015071489 0.09019344 0.07512195 12.863462
## 3 0.07731550 0.08382942 0.03671846 0.008701057 0.13190802 0.12320696 30.757155
## 4 0.15122809 0.07211059 0.15801119 0.096581728 0.20795525 0.11137352  1.232831
## 5 0.13512039 0.07914610 0.12465623 0.078720218 0.20604493 0.12732471  1.101174
## 7 0.15076233 0.07446321 0.16010638 0.092898936 0.20571809 0.11281915  1.530643
## 8 0.16051433 0.07676688 0.14433678 0.110532168 0.23196187 0.12142971  1.397156
##          kurt    sp.ent       sfm       mode   centroid    meanfun     minfun
## 1  274.402906 0.8933694 0.4919178 0.00000000 0.05978098 0.08427911 0.01570167
## 3 1024.927705 0.8463891 0.4789050 0.00000000 0.07731550 0.09870626 0.01565558
## 4    4.177296 0.9633225 0.7272318 0.08387819 0.15122809 0.08896485 0.01779755
## 5    4.333713 0.9719551 0.7835681 0.10426140 0.13512039 0.10639784 0.01693122
## 7    5.987498 0.9675731 0.7626377 0.08619681 0.15076233 0.10594452 0.02622951
## 8    4.766611 0.9592546 0.7198579 0.12832407 0.16051433 0.09305243 0.01775805
##      maxfun     meandom    mindom    maxdom   dfrange    modindx
## 1 0.2758621 0.007812500 0.0078125 0.0078125 0.0000000 0.00000000
## 3 0.2711864 0.007990057 0.0078125 0.0156250 0.0078125 0.04651163
## 4 0.2500000 0.201497396 0.0078125 0.5625000 0.5546875 0.24711908
## 5 0.2666667 0.712812500 0.0078125 5.4843750 5.4765625 0.20827389
## 7 0.2666667 0.479619565 0.0078125 5.3125000 5.3046875 0.12399186
## 8 0.1441441 0.301339286 0.0078125 0.5390625 0.5312500 0.28393665
y <- taprendizaje$genero
datos.test <- model.matrix(genero~.,ttesting)[,-1]
modelo.lasso <- glmnet(x, y, alpha = 1, family = "multinomial") 
plot(modelo.lasso,"lambda", label=TRUE)

modelo.lasso.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
plot(modelo.lasso.cv)

mejor.lambda <- modelo.lasso.cv$lambda.min
mejor.lambda
## [1] 0.001083261
prediccion <- predict(modelo.lasso.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$genero
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##            prediccion
## Actual      Femenino Masculino
##   Femenino       323        12
##   Masculino        4       294
## 
## $`Precisión Global`
## [1] 0.9747235
## 
## $`Error Global`
## [1] 0.02527646
## 
## $`Precisión por categoría`
##  Femenino Masculino 
## 0.9641791 0.9865772 
## 
## $`Precision Positiva`
## [1] 0.9865772
## 
## $`Precision Negativa`
## [1] 0.9641791
## 
## $`Falsos Positivos`
## [1] 0.0358209
## 
## $`Falsos Negativos`
## [1] 0.01342282
## 
## $`Asertividad Positiva`
## [1] 0.9607843
## 
## $`Asertividad Negativa`
## [1] 0.9877676

###Ridge

x <- model.matrix(genero ~ ., taprendizaje)[,-1]
head(x)
##     meanfreq         sd     median         Q25        Q75        IQR      skew
## 1 0.05978098 0.06424127 0.03202691 0.015071489 0.09019344 0.07512195 12.863462
## 3 0.07731550 0.08382942 0.03671846 0.008701057 0.13190802 0.12320696 30.757155
## 4 0.15122809 0.07211059 0.15801119 0.096581728 0.20795525 0.11137352  1.232831
## 5 0.13512039 0.07914610 0.12465623 0.078720218 0.20604493 0.12732471  1.101174
## 7 0.15076233 0.07446321 0.16010638 0.092898936 0.20571809 0.11281915  1.530643
## 8 0.16051433 0.07676688 0.14433678 0.110532168 0.23196187 0.12142971  1.397156
##          kurt    sp.ent       sfm       mode   centroid    meanfun     minfun
## 1  274.402906 0.8933694 0.4919178 0.00000000 0.05978098 0.08427911 0.01570167
## 3 1024.927705 0.8463891 0.4789050 0.00000000 0.07731550 0.09870626 0.01565558
## 4    4.177296 0.9633225 0.7272318 0.08387819 0.15122809 0.08896485 0.01779755
## 5    4.333713 0.9719551 0.7835681 0.10426140 0.13512039 0.10639784 0.01693122
## 7    5.987498 0.9675731 0.7626377 0.08619681 0.15076233 0.10594452 0.02622951
## 8    4.766611 0.9592546 0.7198579 0.12832407 0.16051433 0.09305243 0.01775805
##      maxfun     meandom    mindom    maxdom   dfrange    modindx
## 1 0.2758621 0.007812500 0.0078125 0.0078125 0.0000000 0.00000000
## 3 0.2711864 0.007990057 0.0078125 0.0156250 0.0078125 0.04651163
## 4 0.2500000 0.201497396 0.0078125 0.5625000 0.5546875 0.24711908
## 5 0.2666667 0.712812500 0.0078125 5.4843750 5.4765625 0.20827389
## 7 0.2666667 0.479619565 0.0078125 5.3125000 5.3046875 0.12399186
## 8 0.1441441 0.301339286 0.0078125 0.5390625 0.5312500 0.28393665
y <- taprendizaje$genero
datos.test <- model.matrix(genero~.,ttesting)[,-1]
modelo.ridge <- glmnet(x, y, alpha = 1, family = "multinomial") 
plot(modelo.ridge,"lambda", label=TRUE)

modelo.ridge.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
plot(modelo.ridge.cv)

mejor.lambda <- modelo.ridge.cv$lambda.min
mejor.lambda
## [1] 0.0008993424
prediccion <- predict(modelo.ridge.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$genero
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##            prediccion
## Actual      Femenino Masculino
##   Femenino       323        12
##   Masculino        4       294
## 
## $`Precisión Global`
## [1] 0.9747235
## 
## $`Error Global`
## [1] 0.02527646
## 
## $`Precisión por categoría`
##  Femenino Masculino 
## 0.9641791 0.9865772 
## 
## $`Precision Positiva`
## [1] 0.9865772
## 
## $`Precision Negativa`
## [1] 0.9641791
## 
## $`Falsos Positivos`
## [1] 0.0358209
## 
## $`Falsos Negativos`
## [1] 0.01342282
## 
## $`Asertividad Positiva`
## [1] 0.9607843
## 
## $`Asertividad Negativa`
## [1] 0.9877676
  1. Construya una tabla para los ´ındices anteriores que permita comparar el resultados de los m´etodos Regresi´on Log´ıstica Cl´asica, Ridge y Lasso ¿Cu´al m´etodo es mejor? Campare tambi´en con los resultados de las tareas anteriores. ¿Cu´al m´etodo es mejor?
x <- data.frame("Modelo" = c("Regresion Logistica","Ridge","Lasso"), "Precision Global" = c(0.9763033,0.9763033, 0.9763033), "Error Global" = c(0.02369668,0.02369668,0.02369668), "precision positiva" = c(0.9803922, 0.9803922, 0.9803922), "precision negativa" = c(0.9724771, 0.9724771, 0.9724771), "falsos positivos" = c(0.02752294, 0.02752294, 0.02752294), "falsos negativos" = c(0.01960784, 0.01960784, 0.01960784), "asertividad positiva" = c(0.9708738, 0.9708738, 0.9708738), "asertividad negativa" = c(0.9814815, 0.9814815, 0.9814815))
x
##                Modelo Precision.Global Error.Global precision.positiva
## 1 Regresion Logistica        0.9763033   0.02369668          0.9803922
## 2               Ridge        0.9763033   0.02369668          0.9803922
## 3               Lasso        0.9763033   0.02369668          0.9803922
##   precision.negativa falsos.positivos falsos.negativos asertividad.positiva
## 1          0.9724771       0.02752294       0.01960784            0.9708738
## 2          0.9724771       0.02752294       0.01960784            0.9708738
## 3          0.9724771       0.02752294       0.01960784            0.9708738
##   asertividad.negativa
## 1            0.9814815
## 2            0.9814815
## 3            0.9814815

Los tres modelos curiosamente dan resultados iguales pero analizando la precision global y por categorias estos tres modelos de regresion por igual logran estimar adecuadamente la diferencia en el genero de voz. El metodo trainRSVM “Radial” continua siendo el mas preciso y con menor error global. A pesar de ello bosques aleatorios (0.9763033), potenciaciacion (0.9731438) y xgboosting (0.9794629) se muestran como metodos significativamente precisos, tambien. De hecho, la diferencia respecto al SVM mencionado no resulta significativa. El metodo de Bayes posee el peor desempeno segun la precision global y el error, analizando la matriz de confusion ademas, se denota que este metodo tiene un exceso importante de datos confundidos. El mejor SVM para este ejercicio es con el default kernel, el radial. Posee una precision bastante alta de 0.9873618. Para este ejercicio parece ser el metodo el que mejor se ha ajustado. El arbol de decisio con una precision global de 0.9462875, estuvo mas bajo. Ademas otras estimaciones tardan mas, es decir, redes tarda mas entre mas cantidad de nodos, pero la precision global y el error global en los tres casos de redes se mantienen entre 97 y 98%, y 2% y 3%, respectivamente. De los seis modelos (svm, arbol, 3 redes, kvecinos) tomando precision global como criterio principal y revisando un poco la precision de categorias, SVM radial en este caso tiene la precision mas alta , pero se denota que no existe diferencia significativa, ya que siguiendo la finalidad de este ejercicio se realizan distintas simulaciones para comparar. En la mayoria de los casos la precision de la red neuronal especialmente la de 4 nodos se acerca a la que mejor estimacion de svm radial, pero todas las estimaciones usando k vecinos y redes neuronales con distinta cantidad de nodos, han dado precisiones de entre 96,68% y 98,26% como ocurrio en el de 4 nodos contra 15 nodos, es decir, no se notan diferencias significativas. Los svm con otros kernels tambien han sido bastante acertados, de hecho, puede ser la muestra la que influya en la decision.

Pregunta 8: En esta pregunta utiliza los datos (tumores.csv). Se trata de un conjunto de datos de caracter´ısticas del tumor cerebral que incluye cinco variables de primer orden y ocho de textura y cuatro par´ametros de evaluaci´on de la calidad con el nivel objetivo. La variables son: Media, Varianza, Desviaci´on est´andar, Asimetr´ıa, Kurtosis, Contraste, Energ´ıa, ASM (segundo momento angular), Entrop´ıa, Homogeneidad, Disimilitud, Correlaci´on, Grosor, PSNR (Pico de la relaci´on se˜nal-ruido), SSIM (´Indice de Similitud Estructurada), MSE (Mean Square Error), DC (Coeficiente de Dados) y la variable a predecir tipo (1 = Tumor, 0 = No-Tumor). Realice lo siguiente:

setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase2")
data2<-read.csv("tumores.csv",dec='.',header=T)
head(data2)
##   imagen     media  varianza desviacion.estandar  entropia asimetria  kurtosis
## 1 Image1 23.448517 2538.9856            50.38835 0.6511741  1.984202  5.421042
## 2 Image2  4.398331  834.8530            28.89382 0.9535317  6.495203 43.349355
## 3 Image3  3.244263  642.0592            25.33889 0.9660645  7.772860 61.756034
## 4 Image4  8.511353 1126.2142            33.55911 0.8687651  3.763142 15.107579
## 5 Image5 21.000793 2235.3170            47.27914 0.6847244  1.936029  4.722343
## 6 Image7 11.350555  998.9722            31.60652 0.7611065  2.533920  7.394586
##   contraste   energia       asm homogeneidad disiminitud correlacion      psnr
## 1 181.46771 0.7815569 0.6108312    0.8470333   2.7654114   0.9685761  97.97463
## 2  76.74589 0.9727695 0.9462805    0.9807616   0.5486053   0.9597505 110.34660
## 3  81.75241 0.9801609 0.9607154    0.9850659   0.5404114   0.9442587 112.26630
## 4 362.29121 0.9217862 0.8496899    0.9492953   2.7657252   0.8590271 101.95579
## 5 312.43923 0.8041836 0.6467113    0.8803008   3.0066597   0.9385719  97.63987
## 6 303.94798 0.8542768 0.7297889    0.9023554   3.4405509   0.8664795  99.20658
##        ssim         mse        dc tipo
## 1 0.7770111 0.171163194 0.3039887    1
## 2 0.9779528 0.009913194 0.8390189    1
## 3 0.9853620 0.006371528 0.8497749    1
## 4 0.8810152 0.068437500 0.0000000    0
## 5 0.7663084 0.184878472 0.0000000    0
## 6 0.7948807 0.128888889 0.0000000    0
library(caret)
## Loading required package: lattice
## 
## Attaching package: 'caret'
## The following object is masked from 'package:purrr':
## 
##     lift
data2$tipo <- factor(data1$tipo,ordered = TRUE)
## Error in factor(data1$tipo, ordered = TRUE): object 'data1' not found
data1 <- data2[,-1]
str(data1)
## 'data.frame':    1275 obs. of  17 variables:
##  $ media              : num  23.45 4.4 3.24 8.51 21 ...
##  $ varianza           : num  2539 835 642 1126 2235 ...
##  $ desviacion.estandar: num  50.4 28.9 25.3 33.6 47.3 ...
##  $ entropia           : num  0.651 0.954 0.966 0.869 0.685 ...
##  $ asimetria          : num  1.98 6.5 7.77 3.76 1.94 ...
##  $ kurtosis           : num  5.42 43.35 61.76 15.11 4.72 ...
##  $ contraste          : num  181.5 76.7 81.8 362.3 312.4 ...
##  $ energia            : num  0.782 0.973 0.98 0.922 0.804 ...
##  $ asm                : num  0.611 0.946 0.961 0.85 0.647 ...
##  $ homogeneidad       : num  0.847 0.981 0.985 0.949 0.88 ...
##  $ disiminitud        : num  2.765 0.549 0.54 2.766 3.007 ...
##  $ correlacion        : num  0.969 0.96 0.944 0.859 0.939 ...
##  $ psnr               : num  98 110.3 112.3 102 97.6 ...
##  $ ssim               : num  0.777 0.978 0.985 0.881 0.766 ...
##  $ mse                : num  0.17116 0.00991 0.00637 0.06844 0.18488 ...
##  $ dc                 : num  0.304 0.839 0.85 0 0 ...
##  $ tipo               : int  1 1 1 0 0 0 1 1 1 1 ...
barplot(prop.table(table(data1$tipo)),col=c("orange","blue","green"),main="Distribución de la variable por predecir")

Ejercicio desbalanceado

intrain <- createDataPartition(
  y = data1$tipo,
  p = .75,
  list = FALSE
)
str(intrain)
##  int [1:957, 1] 1 2 3 5 6 7 8 9 10 11 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr "Resample1"
taprendizaje <- data1[ intrain,]
ttesting  <- data1[-intrain,]

nrow(taprendizaje)
## [1] 957
nrow(ttesting)
## [1] 318
  1. Usando la funci´on indices.general(…) vista en clase para la tabla de testing calcule la matriz de confusi´on, la precisi´on global, el error global y la precisi´on de cada una de las clases. Construya una tabla para los ´ındices anteriores que permita comparar los resultados de los m´etodos Regresi´on Log´ıstica Cl´asica, Ridge y Lasso. ¿Cu´al m´etodo es mejor? Campare tambi´en con los resultados de las tareas anteriores. ¿Cu´al m´etodo es mejor?
modelo <- glm(tipo ~ . , data = taprendizaje, family = binomial)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
probabilidades <- predict(modelo, ttesting, type = "response")
head(probabilidades)
##            4           17           18           25           29           31 
## 2.220446e-16 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
prediccion <- rep("No", dim(ttesting)[1])
prediccion[probabilidades > 0.5] = "Si"  # Porque 1=Si entonces P>=0.5 => Si
Actual <- ttesting$tipo
## Matriz de Confusión
MC <- table(Actual, prediccion)
indices.general(MC)
## $`Matriz de Confusión`
##       prediccion
## Actual  No  Si
##      0  19   4
##      1   9 286
## 
## $`Precisión Global`
## [1] 0.9591195
## 
## $`Error Global`
## [1] 0.0408805
## 
## $`Precisión por categoría`
##         0         1 
## 0.8260870 0.9694915 
## 
## $`Precision Positiva`
## [1] 0.9694915
## 
## $`Precision Negativa`
## [1] 0.826087
## 
## $`Falsos Positivos`
## [1] 0.173913
## 
## $`Falsos Negativos`
## [1] 0.03050847
## 
## $`Asertividad Positiva`
## [1] 0.9862069
## 
## $`Asertividad Negativa`
## [1] 0.6785714

#LASSO

x <- model.matrix(tipo ~ ., taprendizaje)[,-1]
head(x)
##        media   varianza desviacion.estandar  entropia asimetria   kurtosis
## 1 23.4485168 2538.98563           50.388348 0.6511741  1.984202   5.421042
## 2  4.3983307  834.85303           28.893823 0.9535317  6.495203  43.349355
## 3  3.2442627  642.05917           25.338886 0.9660645  7.772860  61.756034
## 5 21.0007935 2235.31698           47.279139 0.6847244  1.936029   4.722343
## 6 11.3505554  998.97224           31.606522 0.7611065  2.533920   7.394586
## 7  0.4051361   68.37872            8.269143 0.9947236 20.388025 416.843433
##   contraste   energia       asm homogeneidad disiminitud correlacion      psnr
## 1 181.46771 0.7815569 0.6108312    0.8470333   2.7654114   0.9685761  97.97463
## 2  76.74589 0.9727695 0.9462805    0.9807616   0.5486053   0.9597505 110.34660
## 3  81.75241 0.9801609 0.9607154    0.9850659   0.5404114   0.9442587 112.26630
## 5 312.43923 0.8041836 0.6467113    0.8803008   3.0066597   0.9385719  97.63987
## 6 303.94798 0.8542768 0.7297889    0.9023554   3.4405509   0.8664795  99.20658
## 7  17.78916 0.9969317 0.9938728    0.9978846   0.1144003   0.8861440 111.37119
##        ssim         mse        dc
## 1 0.7770111 0.171163194 0.3039887
## 2 0.9779528 0.009913194 0.8390189
## 3 0.9853620 0.006371528 0.8497749
## 5 0.7663084 0.184878472 0.0000000
## 6 0.7948807 0.128888889 0.0000000
## 7 0.9851754 0.007829861 0.4104575
y <- taprendizaje$tipo
datos.test <- model.matrix(tipo~.,ttesting)[,-1]
modelo.lasso <- glmnet(x, y, alpha = 1, family = "multinomial") 
## Warning: from glmnet Fortran code (error code -100); Convergence for 100th
## lambda value not reached after maxit=100000 iterations; solutions for larger
## lambdas returned
plot(modelo.lasso,"lambda", label=TRUE)

modelo.lasso.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
## Warning: from glmnet Fortran code (error code -100); Convergence for 100th
## lambda value not reached after maxit=100000 iterations; solutions for larger
## lambdas returned
## Warning: from glmnet Fortran code (error code -97); Convergence for 97th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -98); Convergence for 98th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -96); Convergence for 96th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -98); Convergence for 98th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -92); Convergence for 92th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -99); Convergence for 99th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -96); Convergence for 96th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
plot(modelo.lasso.cv)

mejor.lambda <- modelo.lasso.cv$lambda.min
mejor.lambda
## [1] 0.0002184057
prediccion <- predict(modelo.lasso.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$tipo
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##       prediccion
## Actual   0   1
##      0  22   1
##      1   9 286
## 
## $`Precisión Global`
## [1] 0.9685535
## 
## $`Error Global`
## [1] 0.03144654
## 
## $`Precisión por categoría`
##         0         1 
## 0.9565217 0.9694915 
## 
## $`Precision Positiva`
## [1] 0.9694915
## 
## $`Precision Negativa`
## [1] 0.9565217
## 
## $`Falsos Positivos`
## [1] 0.04347826
## 
## $`Falsos Negativos`
## [1] 0.03050847
## 
## $`Asertividad Positiva`
## [1] 0.9965157
## 
## $`Asertividad Negativa`
## [1] 0.7096774

###Ridge

x <- model.matrix(tipo ~ ., taprendizaje)[,-1]
head(x)
##        media   varianza desviacion.estandar  entropia asimetria   kurtosis
## 1 23.4485168 2538.98563           50.388348 0.6511741  1.984202   5.421042
## 2  4.3983307  834.85303           28.893823 0.9535317  6.495203  43.349355
## 3  3.2442627  642.05917           25.338886 0.9660645  7.772860  61.756034
## 5 21.0007935 2235.31698           47.279139 0.6847244  1.936029   4.722343
## 6 11.3505554  998.97224           31.606522 0.7611065  2.533920   7.394586
## 7  0.4051361   68.37872            8.269143 0.9947236 20.388025 416.843433
##   contraste   energia       asm homogeneidad disiminitud correlacion      psnr
## 1 181.46771 0.7815569 0.6108312    0.8470333   2.7654114   0.9685761  97.97463
## 2  76.74589 0.9727695 0.9462805    0.9807616   0.5486053   0.9597505 110.34660
## 3  81.75241 0.9801609 0.9607154    0.9850659   0.5404114   0.9442587 112.26630
## 5 312.43923 0.8041836 0.6467113    0.8803008   3.0066597   0.9385719  97.63987
## 6 303.94798 0.8542768 0.7297889    0.9023554   3.4405509   0.8664795  99.20658
## 7  17.78916 0.9969317 0.9938728    0.9978846   0.1144003   0.8861440 111.37119
##        ssim         mse        dc
## 1 0.7770111 0.171163194 0.3039887
## 2 0.9779528 0.009913194 0.8390189
## 3 0.9853620 0.006371528 0.8497749
## 5 0.7663084 0.184878472 0.0000000
## 6 0.7948807 0.128888889 0.0000000
## 7 0.9851754 0.007829861 0.4104575
y <- taprendizaje$tipo
datos.test <- model.matrix(tipo~.,ttesting)[,-1]
modelo.ridge <- glmnet(x, y, alpha = 1, family = "multinomial") 
## Warning: from glmnet Fortran code (error code -100); Convergence for 100th
## lambda value not reached after maxit=100000 iterations; solutions for larger
## lambdas returned
plot(modelo.ridge,"lambda", label=TRUE)

modelo.ridge.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
## Warning: from glmnet Fortran code (error code -100); Convergence for 100th
## lambda value not reached after maxit=100000 iterations; solutions for larger
## lambdas returned
## Warning: from glmnet Fortran code (error code -99); Convergence for 99th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -100); Convergence for 100th
## lambda value not reached after maxit=100000 iterations; solutions for larger
## lambdas returned
## Warning: from glmnet Fortran code (error code -97); Convergence for 97th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -93); Convergence for 93th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -98); Convergence for 98th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -97); Convergence for 97th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: from glmnet Fortran code (error code -95); Convergence for 95th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
plot(modelo.ridge.cv)

mejor.lambda <- modelo.ridge.cv$lambda.min
mejor.lambda
## [1] 0.0003168692
prediccion <- predict(modelo.ridge.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$tipo
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##       prediccion
## Actual   0   1
##      0  22   1
##      1  10 285
## 
## $`Precisión Global`
## [1] 0.9654088
## 
## $`Error Global`
## [1] 0.03459119
## 
## $`Precisión por categoría`
##         0         1 
## 0.9565217 0.9661017 
## 
## $`Precision Positiva`
## [1] 0.9661017
## 
## $`Precision Negativa`
## [1] 0.9565217
## 
## $`Falsos Positivos`
## [1] 0.04347826
## 
## $`Falsos Negativos`
## [1] 0.03389831
## 
## $`Asertividad Positiva`
## [1] 0.9965035
## 
## $`Asertividad Negativa`
## [1] 0.6875

Los tres modelos presentan una precision global muy alta por encima de 0,98, de hecho la regresion logistica clasica tiene la precision global mas alta de todos los metodos incluyendo tareas pasadas con 0.9874214, pero tiene igualmente problemas identificando los no tumores al tener una precision por categoria en esto de 0.8750000 frente a un 0.9965986 cuando si es tumor. Lasso y ridge mantienen una precision global 0.9811321 que no tiene diferencia significativa respecto a la logistica. Para el caso de tumores la mejor prediccion, anteriormente fue ser con el metodo de bosques aleatorios con una precision global de 0.9842767, seguido de potenciacion con 0.9811321. Cabe resaltar que XGBoosting no logra distinguir bien la variacion en el problema y tiene la peor precision. Cabe destacar tambien que la precision por categoria es alta cuando existe tumor pero de menos de 0,90 cuando no hay tumor. Esto realmente siguiendo los resultados de tareas anteriores que falta datos de no tumores. El segundo peor metodo es el de BayesNinguno estimado, ademas LDA y QDA no estaban logrando ser estimados. Cabe resaltar que por tratarse de tumores los metodos en general no estan logrando estimar correctamente los casos de no tumor.Todos los kernels dan la misma matrix de confusion en el caso de SVM, excepto el linear que permite identificar ambos casos y tiene la precision global mas alta con 0.9716981, pero una asertividad negativa aun baja de 0.7777778.Comparando de forma sencillo los modelos mas acertados en las tareas anteriores, ya que se han dado varios intentos con resultados de toda clase. El SVM linear parece ser en este ejercicio el que mejor esta asimilando los datos para explicar la variabilidad del caso. Tiene una precision global bastante alta. De hecho, todos los casos probables de no tumor los identifica. Aun asi debe indicarse que se trata con tumores, lo implica que se necesita replantear el modelo, dado que se trata de tumores, lo que es mas importante, es probable que se requier un tamano de muestra mas grande para arrojar datos veridicos ya que en este caso los modelos no estan leyendo completamente bien los casos. En el caso de todos los modelos la precision por categoria ha sido especialmente debil al no detectar los no tumores, esto se explica probablemente por la falta de muestra en estos casos.

Pregunta 9: En este ejercicio vamos a predecir n´umeros escritos a mano (Hand Written Digit Recognition), la tabla de de datos est´a en el archivo ZipData 2020.csv. En la figura siguiente se ilustran los datos:

  1. Cargue la tabla de datos ZipData 2020.csv en R.
setwd("C:/Users/rzamoram/Documents/Machine Learning/Mineria de Datos I/Clase3")
data2<-read.csv("ZipData_2020.csv",sep=";",dec='.',header=T)
head(data2)
##   Numero V2 V3 V4     V5     V6     V7     V8     V9    V10    V11    V12
## 1   seis -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -0.631  0.862 -0.167 -1.000
## 2  cinco -1 -1 -1 -0.813 -0.671 -0.809 -0.887 -0.671 -0.853 -1.000 -1.000
## 3 cuatro -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.996  0.147
## 4  siete -1 -1 -1 -1.000 -1.000 -0.273  0.684  0.960  0.450 -0.067 -0.679
## 5   tres -1 -1 -1 -1.000 -1.000 -0.928 -0.204  0.751  0.466  0.234 -0.809
## 6   seis -1 -1 -1 -1.000 -1.000 -0.397  0.983 -0.535 -1.000 -1.000 -1.000
##      V13    V14    V15    V16 V17 V18 V19 V20    V21    V22    V23    V24
## 1 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -0.992
## 2 -0.774 -0.180  0.052 -0.241  -1  -1  -1  -1  0.392  1.000  0.857  0.727
## 3  1.000 -0.189 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.114  0.974  0.917
## 5 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.370  0.739  1.000
## 6 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000  0.692  0.536
##      V25    V26    V27    V28    V29    V30    V31    V32 V33 V34 V35 V36
## 1  0.297  1.000  0.307 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1
## 2  1.000  0.805  0.613  0.613  0.860  1.000  1.000  0.396  -1  -1  -1  -1
## 3 -1.000 -1.000 -0.882  1.000  0.390 -0.811 -1.000 -1.000  -1  -1  -1  -1
## 4  0.734  0.994  1.000  0.973  0.391 -0.421 -0.976 -1.000  -1  -1  -1  -1
## 5  1.000  1.000  1.000  0.644 -0.890 -1.000 -1.000 -1.000  -1  -1  -1  -1
## 6 -0.767 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1
##      V37    V38    V39    V40    V41    V42    V43    V44    V45 V46    V47
## 1 -1.000 -1.000 -1.000 -0.410  1.000  0.986 -0.565 -1.000 -1.000  -1 -1.000
## 2 -0.548  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000   1  1.000
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.715  1.000  0.029  -1 -1.000
## 4 -0.323  0.991  0.622 -0.738 -1.000 -0.639  0.023  0.871  1.000   1 -0.432
## 5 -1.000  0.616  1.000  0.688 -0.455 -0.731  0.659  1.000 -0.287  -1 -1.000
## 6 -1.000 -0.921  0.928 -0.118 -1.000 -1.000 -1.000 -1.000 -1.000  -1 -1.000
##      V48    V49 V50 V51 V52    V53    V54    V55    V56 V57    V58    V59
## 1 -1.000 -1.000  -1  -1  -1 -1.000 -1.000 -0.683  0.825   1  0.562 -1.000
## 2  0.875 -0.957  -1  -1  -1 -0.786  0.961  1.000  1.000   1  0.727  0.403
## 3 -1.000 -1.000  -1  -1  -1 -1.000 -0.888 -0.912 -1.000  -1 -1.000 -0.549
## 4 -1.000 -1.000  -1  -1  -1  0.409  1.000  0.000 -1.000  -1 -1.000 -1.000
## 5 -1.000 -1.000  -1  -1  -1 -1.000 -0.376 -0.186 -0.874  -1 -1.000 -0.014
## 6 -1.000 -1.000  -1  -1  -1 -1.000 -0.394  1.000 -0.596  -1 -1.000 -1.000
##      V60    V61    V62    V63   V64 V65 V66 V67    V68    V69    V70    V71
## 1 -1.000 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -0.938  0.540
## 2  0.403  0.171 -0.314 -0.314 -0.94  -1  -1  -1 -1.000 -0.298  1.000  1.000
## 3  1.000  0.361 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -0.938  0.694  0.057
## 4 -0.842  0.714  1.000 -0.534 -1.00  -1  -1  -1 -0.879  0.965  1.000 -0.713
## 5  1.000 -0.253 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000  0.060  0.900
##      V72    V73    V74    V75    V76    V77    V78    V79 V80 V81 V82 V83
## 1  1.000  0.778 -0.715 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1
## 2  1.000  0.440  0.056 -0.755 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1
## 3 -1.000 -1.000 -1.000 -0.382  1.000  0.511 -1.000 -1.000  -1  -1  -1  -1
## 4 -1.000 -1.000 -1.000 -1.000 -0.606  0.977  0.695 -0.906  -1  -1  -1  -1
## 5 -1.000 -1.000 -0.978  0.501  1.000 -0.540 -1.000 -1.000  -1  -1  -1  -1
## 6 -0.951 -1.000 -1.000 -1.000 -0.647  0.455 -0.333 -1.000  -1  -1  -1  -1
##      V84    V85    V86    V87    V88    V89    V90    V91    V92    V93    V94
## 1 -1.000 -1.000  0.100  1.000  0.922 -0.439 -1.000 -1.000 -1.000 -1.000 -1.000
## 2 -1.000  0.366  1.000  1.000  1.000  1.000  1.000  0.889 -0.081 -0.920 -1.000
## 3 -1.000 -0.311  1.000 -0.043 -1.000 -1.000 -1.000 -0.648  1.000  0.644 -1.000
## 4 -0.528  1.000  0.931 -0.888 -1.000 -1.000 -1.000 -0.949  0.559  0.984 -0.363
## 5 -1.000 -1.000 -1.000 -0.998 -0.341  0.296  0.371  1.000  0.417 -0.989 -1.000
## 6 -1.000 -1.000  0.259  0.676 -1.000 -1.000 -1.000 -0.984  0.677  0.981  0.551
##   V95 V96 V97 V98 V99  V100   V101   V102   V103   V104   V105  V106   V107
## 1  -1  -1  -1  -1  -1 -1.00 -0.257  0.950  1.000 -0.162 -1.000 -1.00 -1.000
## 2  -1  -1  -1  -1  -1 -1.00 -0.396  0.886  0.974  0.851  0.851  0.95  1.000
## 3  -1  -1  -1  -1  -1 -1.00  0.489  1.000 -0.493 -1.000 -1.000 -1.00 -0.564
## 4  -1  -1  -1  -1  -1 -0.97 -0.266 -0.555 -1.000 -1.000 -1.000 -1.00 -0.186
## 5  -1  -1  -1  -1  -1 -1.00 -1.000 -1.000 -0.008  1.000  1.000  1.00  1.000
## 6  -1  -1  -1  -1  -1 -1.00 -0.994  0.699  0.305 -1.000 -1.000 -1.00 -0.499
##     V108   V109   V110 V111 V112 V113 V114 V115   V116   V117   V118   V119
## 1 -0.987 -0.714 -0.832   -1   -1   -1   -1   -1 -0.797  0.909  1.000  0.300
## 2  1.000  0.539 -0.754   -1   -1   -1   -1   -1 -1.000 -1.000 -0.886 -0.505
## 3  1.000  0.693 -1.000   -1   -1   -1   -1   -1 -0.966  0.988  1.000 -0.893
## 4  1.000  0.488 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000 -1.000
## 5  0.761 -0.731 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000  0.242
## 6  1.000 -0.092  0.751   -1   -1   -1   -1   -1 -1.000 -0.923  0.966 -0.107
##     V120 V121   V122   V123  V124   V125   V126   V127 V128 V129 V130 V131
## 1 -0.961   -1 -1.000 -0.550 0.485  0.996  0.867  0.092   -1   -1   -1   -1
## 2 -1.000   -1 -0.649  0.405 1.000  1.000  0.653 -0.838   -1   -1   -1   -1
## 3 -1.000   -1 -1.000 -0.397 1.000  0.903 -0.977 -1.000   -1   -1   -1   -1
## 4 -1.000   -1 -1.000  0.697 0.992 -0.458 -1.000 -1.000   -1   -1   -1   -1
## 5  1.000    1  0.319  0.259 1.000  0.742 -0.757 -1.000   -1   -1   -1   -1
## 6 -1.000   -1 -1.000 -0.300 0.854 -0.382  0.617 -1.000   -1   -1   -1   -1
##     V132   V133   V134   V135   V136   V137   V138   V139   V140   V141   V142
## 1  0.278  1.000  0.877 -0.824 -1.000 -0.905  0.145  0.977  1.000  1.000  1.000
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.550  0.993  1.000
## 3 -0.559  1.000  1.000 -0.297 -1.000 -1.000 -1.000 -0.611  1.000  0.873 -0.698
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.341  1.000  0.608 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 -0.975 -0.467 -0.989 -1.000 -1.000 -0.171  0.998  0.669
## 6 -1.000 -0.409  1.000 -0.529 -1.000 -1.000 -1.000  0.048  0.614 -0.268  0.544
##     V143   V144 V145  V146   V147   V148   V149   V150   V151   V152   V153
## 1  0.990 -0.745   -1 -1.00 -0.950  0.847  1.000  0.327 -1.000 -1.000  0.355
## 2  0.618 -0.869   -1 -0.96 -0.512  0.134 -0.343 -0.796 -1.000 -1.000 -1.000
## 3 -0.552 -1.000   -1 -1.00 -1.000 -0.126  1.000  1.000  0.766 -0.764 -1.000
## 4 -1.000 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.945 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000   -1 -1.00 -1.000 -1.000  0.050  0.971 -0.839 -1.000 -1.000
##     V154   V155   V156   V157   V158   V159   V160 V161   V162  V163   V164
## 1  1.000  0.655 -0.109 -0.185  1.000  0.988 -0.723   -1 -1.000 -0.63  1.000
## 2 -1.000 -1.000 -1.000 -0.432  0.994  1.000  0.223   -1  0.426  1.00  1.000
## 3 -1.000 -0.577  1.000  0.933  0.484 -0.197 -1.000   -1 -1.000 -1.00 -0.818
## 4  0.471  0.998 -0.416 -1.000 -1.000 -1.000 -1.000   -1 -1.000 -1.00 -1.000
## 5 -1.000 -1.000 -1.000  0.228  1.000  0.038 -1.000   -1 -1.000 -1.00 -1.000
## 6 -1.000  0.172  0.526 -0.003  0.307 -1.000 -1.000   -1 -1.000 -1.00 -1.000
##     V165   V166   V167   V168   V169   V170   V171   V172   V173   V174   V175
## 1  1.000  0.068 -0.925  0.113  0.960  0.308 -0.884 -1.000 -0.075  1.000  0.641
## 2  1.000  0.214 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.292  1.000
## 3 -0.355  0.334  1.000  0.868 -0.289 -0.677 -0.596  1.000  1.000  1.000 -0.581
## 4 -1.000 -1.000 -1.000 -1.000 -0.644  0.963  0.590 -0.999 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.826  0.918  0.933
## 6  0.398  0.459 -1.000 -1.000 -1.000 -1.000  0.372  0.555  0.520 -0.045 -1.000
##     V176  V177   V178   V179   V180   V181   V182   V183 V184   V185   V186
## 1 -0.995 -1.00 -1.000 -0.677  1.000  1.000  0.753  0.341    1  0.707 -0.942
## 2  0.967 -0.88  0.449  1.000  0.896 -0.094 -0.750 -1.000   -1 -1.000 -1.000
## 3 -1.000 -1.00 -1.000 -1.000 -1.000 -1.000 -0.954  0.118    1  1.000  1.000
## 4 -1.000 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000   -1  0.061  1.000
## 5 -0.794 -1.00 -1.000 -1.000 -0.666  0.337  0.224 -0.908   -1 -1.000 -1.000
## 6 -1.000 -1.00 -1.000 -1.000 -1.000  0.671  0.176 -1.000   -1 -1.000 -1.000
##     V187   V188   V189   V190   V191   V192   V193   V194   V195   V196   V197
## 1 -1.000 -1.000  0.545  1.000  0.027 -1.000 -1.000 -1.000 -0.903  0.792  1.000
## 2 -1.000 -1.000 -1.000 -0.627  1.000  1.000  0.198 -0.105  1.000  1.000  1.000
## 3  1.000  1.000  0.973 -0.092 -0.995 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -0.079 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000  0.418  1.000 -0.258 -1.000 -1.000 -0.246  1.000  1.000
## 6  0.236  0.934  0.971 -0.712 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.763
##     V198   V199   V200   V201   V202   V203   V204   V205   V206   V207   V208
## 1  1.000  1.000  1.000  0.536  0.184  0.812  0.837  0.978  0.864 -0.630 -1.000
## 2  0.639 -0.168 -0.314 -0.446 -1.000 -1.000 -0.999 -0.337  0.147  0.996  1.000
## 3 -1.000 -0.993 -0.464  0.046  0.290  0.457  1.000  0.721 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000  0.773  0.958 -0.714 -1.000 -1.000 -1.000 -1.000 -1.000
## 5  0.355 -0.958 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.077  1.000  0.344
## 6  0.084 -1.000 -1.000 -1.000 -1.000  0.073  1.000  0.265 -1.000 -1.000 -1.000
##     V209   V210   V211   V212   V213   V214   V215   V216   V217   V218   V219
## 1 -1.000 -1.000 -1.000 -0.452  0.828  1.000  1.000  1.000  1.000  1.000  1.000
## 2  0.667 -0.808  0.065  0.993  1.000  1.000  1.000  1.000  0.996  0.970  0.970
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.426
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.545  0.989  0.432 -1.000
## 5 -1.000 -1.000  0.075  1.000  1.000  0.649  0.256 -0.200 -0.351 -0.733 -0.733
## 6 -1.000 -1.000 -1.000 -1.000  0.563  0.210 -1.000 -1.000 -0.930 -0.127  0.890
##     V220   V221   V222 V223   V224   V225 V226   V227   V228   V229   V230
## 1  1.000  1.000  0.135   -1 -1.000 -1.000   -1 -1.000 -1.000 -0.483  0.813
## 2  0.970  0.998  1.000    1  1.000  0.109   -1 -1.000 -0.830 -0.242  0.350
## 3  1.000  0.555 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000
## 5 -0.733 -0.433  0.649    1  0.093 -1.000   -1 -0.959 -0.062  0.821  1.000
## 6  0.935 -0.845 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000  0.093  0.793
##     V231   V232   V233   V234   V235   V236   V237   V238   V239   V240  V241
## 1  1.000  1.000  1.000  1.000  1.000  1.000  0.219 -0.943 -1.000 -1.000 -1.00
## 2  0.800  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.616 -0.93
## 3 -1.000 -1.000 -1.000 -1.000  0.024  1.000  0.388 -1.000 -1.000 -1.000 -1.00
## 4 -1.000 -0.348  1.000  0.798 -0.935 -1.000 -1.000 -1.000 -1.000 -1.000 -1.00
## 5  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.583 -0.843 -1.00
## 6 -0.205  0.214  0.746  0.918  0.692  0.954 -0.882 -1.000 -1.000 -1.000 -1.00
##   V242 V243 V244   V245   V246   V247   V248   V249   V250   V251   V252   V253
## 1   -1   -1   -1 -1.000 -0.974 -0.429  0.304  0.823  1.000  0.482 -0.474 -0.991
## 2   -1   -1   -1 -1.000 -1.000 -0.858 -0.671 -0.671 -0.033  0.761  0.762  0.126
## 3   -1   -1   -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.109  1.000 -0.179
## 4   -1   -1   -1 -1.000 -1.000 -1.000 -0.318  1.000  0.536 -0.987 -1.000 -1.000
## 5   -1   -1   -1 -0.877 -0.326  0.174  0.466  0.639  1.000  1.000  0.791  0.439
## 6   -1   -1   -1 -0.898  0.323  1.000  0.803  0.015 -0.862 -0.871 -0.437 -1.000
##     V254   V255   V256 V257
## 1 -1.000 -1.000 -1.000   -1
## 2 -0.095 -0.671 -0.828   -1
## 3 -1.000 -1.000 -1.000   -1
## 4 -1.000 -1.000 -1.000   -1
## 5 -0.199 -0.883 -1.000   -1
## 6 -1.000 -1.000 -1.000   -1
str(data2)
## 'data.frame':    9298 obs. of  257 variables:
##  $ Numero: Factor w/ 10 levels "cero","cinco",..: 7 2 3 8 9 7 9 10 1 10 ...
##  $ V2    : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V3    : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V4    : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V5    : num  -1 -0.813 -1 -1 -1 -1 -0.83 -1 -1 -1 ...
##  $ V6    : num  -1 -0.671 -1 -1 -1 -1 0.442 -1 -1 -1 ...
##  $ V7    : num  -1 -0.809 -1 -0.273 -0.928 -0.397 1 -1 -0.454 -1 ...
##  $ V8    : num  -1 -0.887 -1 0.684 -0.204 0.983 1 -1 0.879 -1 ...
##  $ V9    : num  -0.631 -0.671 -1 0.96 0.751 -0.535 0.479 0.51 -0.745 -0.909 ...
##  $ V10   : num  0.862 -0.853 -1 0.45 0.466 -1 -0.328 -0.213 -1 0.801 ...
##  $ V11   : num  -0.167 -1 -0.996 -0.067 0.234 -1 -0.947 -1 -1 -0.899 ...
##  $ V12   : num  -1 -1 0.147 -0.679 -0.809 -1 -1 -1 -1 -1 ...
##  $ V13   : num  -1 -0.774 1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V14   : num  -1 -0.18 -0.189 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V15   : num  -1 0.052 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V16   : num  -1 -0.241 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V17   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V18   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V19   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V20   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V21   : num  -1 0.392 -1 -1 -1 -1 -0.025 -1 -1 -1 ...
##  $ V22   : num  -1 1 -1 -0.114 -0.37 -1 0.519 -1 -0.716 -1 ...
##  $ V23   : num  -1 0.857 -1 0.974 0.739 0.692 0.124 -1 0.804 -1 ...
##  $ V24   : num  -0.992 0.727 -1 0.917 1 0.536 0.339 -1 1 -1 ...
##  $ V25   : num  0.297 1 -1 0.734 1 -0.767 0.762 0.292 0.42 -0.405 ...
##  $ V26   : num  1 0.805 -1 0.994 1 -1 1 0.792 -0.664 1 ...
##  $ V27   : num  0.307 0.613 -0.882 1 1 -1 0.456 -0.987 -1 -0.396 ...
##  $ V28   : num  -1 0.613 1 0.973 0.644 -1 -0.707 -1 -1 -1 ...
##  $ V29   : num  -1 0.86 0.39 0.391 -0.89 -1 -1 -1 -1 -1 ...
##  $ V30   : num  -1 1 -0.811 -0.421 -1 -1 -1 -1 -1 -1 ...
##  $ V31   : num  -1 1 -1 -0.976 -1 -1 -1 -1 -1 -1 ...
##  $ V32   : num  -1 0.396 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V33   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V34   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V35   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V36   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V37   : num  -1 -0.548 -1 -0.323 -1 -1 -1 -1 -0.978 -1 ...
##  $ V38   : num  -1 1 -1 0.991 0.616 -0.921 -1 -1 0.713 -1 ...
##  $ V39   : num  -1 1 -1 0.622 1 0.928 -1 -1 1 -1 ...
##  $ V40   : num  -0.41 1 -1 -0.738 0.688 -0.118 -1 -1 0.027 -1 ...
##  $ V41   : num  1 1 -1 -1 -0.455 -1 -0.965 0.56 0.408 -0.072 ...
##  $ V42   : num  0.986 1 -1 -0.639 -0.731 -1 -0.086 0.975 0.947 1 ...
##  $ V43   : num  -0.565 1 -0.715 0.023 0.659 -1 0.843 -0.873 0.56 -0.468 ...
##  $ V44   : num  -1 1 1 0.871 1 -1 0.681 -1 -0.538 -1 ...
##  $ V45   : num  -1 1 0.029 1 -0.287 -1 -0.955 -1 -1 -1 ...
##  $ V46   : num  -1 1 -1 1 -1 -1 -1 -1 -1 -1 ...
##  $ V47   : num  -1 1 -1 -0.432 -1 -1 -1 -1 -1 -1 ...
##  $ V48   : num  -1 0.875 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V49   : num  -1 -0.957 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V50   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V51   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V52   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V53   : num  -1 -0.786 -1 0.409 -1 -1 -1 -1 -0.118 -1 ...
##  $ V54   : num  -1 0.961 -0.888 1 -0.376 -0.394 -1 -1 1 -1 ...
##  $ V55   : num  -0.683 1 -0.912 0 -0.186 1 -1 -1 0.665 -1 ...
##  $ V56   : num  0.825 1 -1 -1 -0.874 -0.596 -1 -1 -0.902 -1 ...
##  $ V57   : num  1 1 -1 -1 -1 -1 -1 0.745 -0.969 0.057 ...
##  $ V58   : num  0.562 0.727 -1 -1 -1 -1 -1 0.999 -0.36 1 ...
##  $ V59   : num  -1 0.403 -0.549 -1 -0.014 -1 -0.467 -0.748 0.805 -0.623 ...
##  $ V60   : num  -1 0.403 1 -0.842 1 -1 1 -1 0.987 -1 ...
##  $ V61   : num  -1 0.171 0.361 0.714 -0.253 -1 -0.279 -1 0.327 -1 ...
##  $ V62   : num  -1 -0.314 -1 1 -1 -1 -1 -1 -0.797 -1 ...
##  $ V63   : num  -1 -0.314 -1 -0.534 -1 -1 -1 -1 -1 -1 ...
##  $ V64   : num  -1 -0.94 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V65   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V66   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V67   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V68   : num  -1 -1 -1 -0.879 -1 -1 -1 -1 -0.935 -1 ...
##  $ V69   : num  -1 -0.298 -0.938 0.965 -1 -1 -1 -1 0.764 -1 ...
##  $ V70   : num  -0.938 1 0.694 1 -1 0.06 -1 -1 1 -1 ...
##  $ V71   : num  0.54 1 0.057 -0.713 -1 0.9 -1 -1 -0.367 -1 ...
##  $ V72   : num  1 1 -1 -1 -1 -0.951 -1 -1 -1 -1 ...
##  $ V73   : num  0.778 0.44 -1 -1 -1 -1 -1 0.596 -1 0.288 ...
##  $ V74   : num  -0.715 0.056 -1 -1 -0.978 -1 -1 1 -1 1 ...
##  $ V75   : num  -1 -0.755 -0.382 -1 0.501 -1 -0.719 -0.601 -0.914 -0.683 ...
##  $ V76   : num  -1 -1 1 -0.606 1 -0.647 1 -1 -0.256 -1 ...
##  $ V77   : num  -1 -1 0.511 0.977 -0.54 0.455 -0.203 -1 0.833 -1 ...
##  $ V78   : num  -1 -1 -1 0.695 -1 -0.333 -1 -1 0.778 -1 ...
##  $ V79   : num  -1 -1 -1 -0.906 -1 -1 -1 -1 -0.22 -1 ...
##  $ V80   : num  -1 -1 -1 -1 -1 -1 -1 -1 -0.992 -1 ...
##  $ V81   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V82   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V83   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V84   : num  -1 -1 -1 -0.528 -1 -1 -1 -1 -0.256 -1 ...
##  $ V85   : num  -1 0.366 -0.311 1 -1 -1 -1 -1 1 -1 ...
##  $ V86   : num  0.1 1 1 0.931 -1 0.259 -1 -1 0.538 -1 ...
##  $ V87   : num  1 1 -0.043 -0.888 -0.998 0.676 -1 -1 -0.986 -1 ...
##  $ V88   : num  0.922 1 -1 -1 -0.341 -1 -1 -1 -1 -1 ...
##  $ V89   : num  -0.439 1 -1 -1 0.296 -1 -1 0.714 -1 0.253 ...
##  $ V90   : num  -1 1 -1 -1 0.371 -1 -0.786 1 -1 1 ...
##  $ V91   : num  -1 0.889 -0.648 -0.949 1 -0.984 0.504 -0.585 -1 -0.647 ...
##  $ V92   : num  -1 -0.081 1 0.559 0.417 0.677 0.945 -1 -1 -1 ...
##  $ V93   : num  -1 -0.92 0.644 0.984 -0.989 0.981 -0.801 -1 -0.837 -1 ...
##  $ V94   : num  -1 -1 -1 -0.363 -1 0.551 -1 -1 0.551 -1 ...
##  $ V95   : num  -1 -1 -1 -1 -1 -1 -1 -1 1 -1 ...
##  $ V96   : num  -1 -1 -1 -1 -1 -1 -1 -1 -0.285 -1 ...
##  $ V97   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V98   : num  -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
##  $ V99   : num  -1 -1 -1 -1 -1 -1 -1 -1 -0.936 -1 ...
##   [list output truncated]
barplot(prop.table(table(data2$Numero)), main="Distribución de la variable por predecir")

  1. Usando los m´etodos Regresi´on Log´ıstica Cl´asica, Ridge y Lasso genere un modelos predictivos y los par´ametros que usted considere m´as conveniente para generar un modelo predictivo para la tabla ZipData 2020.csv usando el 80 % de los datos para la tabla aprendizaje y un 20 % para la tabla testing, luego calcule para los datos de testing la matriz de confusi´on, la precisi´on global y la precisi´on para cada una de las categor´ıas. ¿Son buenos los resultados? Explique.
muestra <- sample(1:nrow(data2),floor(nrow(data2)*0.20))
ttesting <- data2[muestra,]
taprendizaje <- data2[-muestra,]

nrow(ttesting)
## [1] 1859
nrow(taprendizaje)
## [1] 7439
modelo <- glm(Numero ~ . , data = taprendizaje, family = binomial)
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
probabilidades <- predict(modelo, ttesting, type = "response")
head(probabilidades)
## 8624 6808 7274 3516 6821 2152 
##    1    1    1    1    1    1
prediccion <- rep("No", dim(ttesting)[1])
prediccion[probabilidades > 0.5] = "Si"  # Porque 1=Si entonces P>=0.5 => Si
Actual <- ttesting$Numero
## Matriz de Confusión
MC <- table(Actual, prediccion)
indices.general(MC)
## $`Matriz de Confusión`
##         prediccion
## Actual    No  Si
##   cero   304  17
##   cinco    1 126
##   cuatro   2 156
##   dos      5 191
##   nueve    1 155
##   ocho     6 134
##   seis     6 140
##   siete    2 167
##   tres     8 174
##   uno      0 264
## 
## $`Precisión Global`
## [1] 0.2313072
## 
## $`Error Global`
## [1] 0.7686928
## 
## $`Precisión por categoría`
##      cero     cinco    cuatro       dos     nueve      ocho      seis     siete 
## 0.9470405 0.9921260 1.9240506 0.6428571 1.9487179 0.9000000 2.0821918 0.7455621 
##      tres       uno 
## 1.6703297 0.4772727 
## 
## $`Precision Positiva`
## [1] 0.992126
## 
## $`Precision Negativa`
## [1] 0.9470405
## 
## $`Falsos Positivos`
## [1] 0.0529595
## 
## $`Falsos Negativos`
## [1] 0.007874016
## 
## $`Asertividad Positiva`
## [1] 0.8811189
## 
## $`Asertividad Negativa`
## [1] 0.9967213

#LASSO

x <- model.matrix(Numero ~ ., taprendizaje)[,-1]
head(x)
##   V2 V3 V4     V5     V6     V7     V8     V9    V10    V11    V12    V13
## 1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -0.631  0.862 -0.167 -1.000 -1.000
## 2 -1 -1 -1 -0.813 -0.671 -0.809 -0.887 -0.671 -0.853 -1.000 -1.000 -0.774
## 3 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.996  0.147  1.000
## 4 -1 -1 -1 -1.000 -1.000 -0.273  0.684  0.960  0.450 -0.067 -0.679 -1.000
## 5 -1 -1 -1 -1.000 -1.000 -0.928 -0.204  0.751  0.466  0.234 -0.809 -1.000
## 6 -1 -1 -1 -1.000 -1.000 -0.397  0.983 -0.535 -1.000 -1.000 -1.000 -1.000
##      V14    V15    V16 V17 V18 V19 V20    V21    V22    V23    V24    V25
## 1 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -0.992  0.297
## 2 -0.180  0.052 -0.241  -1  -1  -1  -1  0.392  1.000  0.857  0.727  1.000
## 3 -0.189 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.114  0.974  0.917  0.734
## 5 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.370  0.739  1.000  1.000
## 6 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000  0.692  0.536 -0.767
##      V26    V27    V28    V29    V30    V31    V32 V33 V34 V35 V36    V37
## 1  1.000  0.307 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 2  0.805  0.613  0.613  0.860  1.000  1.000  0.396  -1  -1  -1  -1 -0.548
## 3 -1.000 -0.882  1.000  0.390 -0.811 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 4  0.994  1.000  0.973  0.391 -0.421 -0.976 -1.000  -1  -1  -1  -1 -0.323
## 5  1.000  1.000  0.644 -0.890 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 6 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
##      V38    V39    V40    V41    V42    V43    V44    V45 V46    V47    V48
## 1 -1.000 -1.000 -0.410  1.000  0.986 -0.565 -1.000 -1.000  -1 -1.000 -1.000
## 2  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000   1  1.000  0.875
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -0.715  1.000  0.029  -1 -1.000 -1.000
## 4  0.991  0.622 -0.738 -1.000 -0.639  0.023  0.871  1.000   1 -0.432 -1.000
## 5  0.616  1.000  0.688 -0.455 -0.731  0.659  1.000 -0.287  -1 -1.000 -1.000
## 6 -0.921  0.928 -0.118 -1.000 -1.000 -1.000 -1.000 -1.000  -1 -1.000 -1.000
##      V49 V50 V51 V52    V53    V54    V55    V56 V57    V58    V59    V60
## 1 -1.000  -1  -1  -1 -1.000 -1.000 -0.683  0.825   1  0.562 -1.000 -1.000
## 2 -0.957  -1  -1  -1 -0.786  0.961  1.000  1.000   1  0.727  0.403  0.403
## 3 -1.000  -1  -1  -1 -1.000 -0.888 -0.912 -1.000  -1 -1.000 -0.549  1.000
## 4 -1.000  -1  -1  -1  0.409  1.000  0.000 -1.000  -1 -1.000 -1.000 -0.842
## 5 -1.000  -1  -1  -1 -1.000 -0.376 -0.186 -0.874  -1 -1.000 -0.014  1.000
## 6 -1.000  -1  -1  -1 -1.000 -0.394  1.000 -0.596  -1 -1.000 -1.000 -1.000
##      V61    V62    V63   V64 V65 V66 V67    V68    V69    V70    V71    V72
## 1 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -0.938  0.540  1.000
## 2  0.171 -0.314 -0.314 -0.94  -1  -1  -1 -1.000 -0.298  1.000  1.000  1.000
## 3  0.361 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -0.938  0.694  0.057 -1.000
## 4  0.714  1.000 -0.534 -1.00  -1  -1  -1 -0.879  0.965  1.000 -0.713 -1.000
## 5 -0.253 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000  0.060  0.900 -0.951
##      V73    V74    V75    V76    V77    V78    V79 V80 V81 V82 V83    V84
## 1  0.778 -0.715 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 2  0.440  0.056 -0.755 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 3 -1.000 -1.000 -0.382  1.000  0.511 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 4 -1.000 -1.000 -1.000 -0.606  0.977  0.695 -0.906  -1  -1  -1  -1 -0.528
## 5 -1.000 -0.978  0.501  1.000 -0.540 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 6 -1.000 -1.000 -1.000 -0.647  0.455 -0.333 -1.000  -1  -1  -1  -1 -1.000
##      V85    V86    V87    V88    V89    V90    V91    V92    V93    V94 V95 V96
## 1 -1.000  0.100  1.000  0.922 -0.439 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1
## 2  0.366  1.000  1.000  1.000  1.000  1.000  0.889 -0.081 -0.920 -1.000  -1  -1
## 3 -0.311  1.000 -0.043 -1.000 -1.000 -1.000 -0.648  1.000  0.644 -1.000  -1  -1
## 4  1.000  0.931 -0.888 -1.000 -1.000 -1.000 -0.949  0.559  0.984 -0.363  -1  -1
## 5 -1.000 -1.000 -0.998 -0.341  0.296  0.371  1.000  0.417 -0.989 -1.000  -1  -1
## 6 -1.000  0.259  0.676 -1.000 -1.000 -1.000 -0.984  0.677  0.981  0.551  -1  -1
##   V97 V98 V99  V100   V101   V102   V103   V104   V105  V106   V107   V108
## 1  -1  -1  -1 -1.00 -0.257  0.950  1.000 -0.162 -1.000 -1.00 -1.000 -0.987
## 2  -1  -1  -1 -1.00 -0.396  0.886  0.974  0.851  0.851  0.95  1.000  1.000
## 3  -1  -1  -1 -1.00  0.489  1.000 -0.493 -1.000 -1.000 -1.00 -0.564  1.000
## 4  -1  -1  -1 -0.97 -0.266 -0.555 -1.000 -1.000 -1.000 -1.00 -0.186  1.000
## 5  -1  -1  -1 -1.00 -1.000 -1.000 -0.008  1.000  1.000  1.00  1.000  0.761
## 6  -1  -1  -1 -1.00 -0.994  0.699  0.305 -1.000 -1.000 -1.00 -0.499  1.000
##     V109   V110 V111 V112 V113 V114 V115   V116   V117   V118   V119   V120
## 1 -0.714 -0.832   -1   -1   -1   -1   -1 -0.797  0.909  1.000  0.300 -0.961
## 2  0.539 -0.754   -1   -1   -1   -1   -1 -1.000 -1.000 -0.886 -0.505 -1.000
## 3  0.693 -1.000   -1   -1   -1   -1   -1 -0.966  0.988  1.000 -0.893 -1.000
## 4  0.488 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.731 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000  0.242  1.000
## 6 -0.092  0.751   -1   -1   -1   -1   -1 -1.000 -0.923  0.966 -0.107 -1.000
##   V121   V122   V123  V124   V125   V126   V127 V128 V129 V130 V131   V132
## 1   -1 -1.000 -0.550 0.485  0.996  0.867  0.092   -1   -1   -1   -1  0.278
## 2   -1 -0.649  0.405 1.000  1.000  0.653 -0.838   -1   -1   -1   -1 -1.000
## 3   -1 -1.000 -0.397 1.000  0.903 -0.977 -1.000   -1   -1   -1   -1 -0.559
## 4   -1 -1.000  0.697 0.992 -0.458 -1.000 -1.000   -1   -1   -1   -1 -1.000
## 5    1  0.319  0.259 1.000  0.742 -0.757 -1.000   -1   -1   -1   -1 -1.000
## 6   -1 -1.000 -0.300 0.854 -0.382  0.617 -1.000   -1   -1   -1   -1 -1.000
##     V133   V134   V135   V136   V137   V138   V139   V140   V141   V142   V143
## 1  1.000  0.877 -0.824 -1.000 -0.905  0.145  0.977  1.000  1.000  1.000  0.990
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.550  0.993  1.000  0.618
## 3  1.000  1.000 -0.297 -1.000 -1.000 -1.000 -0.611  1.000  0.873 -0.698 -0.552
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -0.341  1.000  0.608 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -0.975 -0.467 -0.989 -1.000 -1.000 -0.171  0.998  0.669 -0.945
## 6 -0.409  1.000 -0.529 -1.000 -1.000 -1.000  0.048  0.614 -0.268  0.544 -1.000
##     V144 V145  V146   V147   V148   V149   V150   V151   V152   V153   V154
## 1 -0.745   -1 -1.00 -0.950  0.847  1.000  0.327 -1.000 -1.000  0.355  1.000
## 2 -0.869   -1 -0.96 -0.512  0.134 -0.343 -0.796 -1.000 -1.000 -1.000 -1.000
## 3 -1.000   -1 -1.00 -1.000 -0.126  1.000  1.000  0.766 -0.764 -1.000 -1.000
## 4 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.471
## 5 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000   -1 -1.00 -1.000 -1.000  0.050  0.971 -0.839 -1.000 -1.000 -1.000
##     V155   V156   V157   V158   V159   V160 V161   V162  V163   V164   V165
## 1  0.655 -0.109 -0.185  1.000  0.988 -0.723   -1 -1.000 -0.63  1.000  1.000
## 2 -1.000 -1.000 -0.432  0.994  1.000  0.223   -1  0.426  1.00  1.000  1.000
## 3 -0.577  1.000  0.933  0.484 -0.197 -1.000   -1 -1.000 -1.00 -0.818 -0.355
## 4  0.998 -0.416 -1.000 -1.000 -1.000 -1.000   -1 -1.000 -1.00 -1.000 -1.000
## 5 -1.000 -1.000  0.228  1.000  0.038 -1.000   -1 -1.000 -1.00 -1.000 -1.000
## 6  0.172  0.526 -0.003  0.307 -1.000 -1.000   -1 -1.000 -1.00 -1.000  0.398
##     V166   V167   V168   V169   V170   V171   V172   V173   V174   V175   V176
## 1  0.068 -0.925  0.113  0.960  0.308 -0.884 -1.000 -0.075  1.000  0.641 -0.995
## 2  0.214 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.292  1.000  0.967
## 3  0.334  1.000  0.868 -0.289 -0.677 -0.596  1.000  1.000  1.000 -0.581 -1.000
## 4 -1.000 -1.000 -1.000 -0.644  0.963  0.590 -0.999 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.826  0.918  0.933 -0.794
## 6  0.459 -1.000 -1.000 -1.000 -1.000  0.372  0.555  0.520 -0.045 -1.000 -1.000
##    V177   V178   V179   V180   V181   V182   V183 V184   V185   V186   V187
## 1 -1.00 -1.000 -0.677  1.000  1.000  0.753  0.341    1  0.707 -0.942 -1.000
## 2 -0.88  0.449  1.000  0.896 -0.094 -0.750 -1.000   -1 -1.000 -1.000 -1.000
## 3 -1.00 -1.000 -1.000 -1.000 -1.000 -0.954  0.118    1  1.000  1.000  1.000
## 4 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000   -1  0.061  1.000 -0.079
## 5 -1.00 -1.000 -1.000 -0.666  0.337  0.224 -0.908   -1 -1.000 -1.000 -1.000
## 6 -1.00 -1.000 -1.000 -1.000  0.671  0.176 -1.000   -1 -1.000 -1.000  0.236
##     V188   V189   V190   V191   V192   V193   V194   V195   V196   V197   V198
## 1 -1.000  0.545  1.000  0.027 -1.000 -1.000 -1.000 -0.903  0.792  1.000  1.000
## 2 -1.000 -1.000 -0.627  1.000  1.000  0.198 -0.105  1.000  1.000  1.000  0.639
## 3  1.000  0.973 -0.092 -0.995 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000  0.418  1.000 -0.258 -1.000 -1.000 -0.246  1.000  1.000  0.355
## 6  0.934  0.971 -0.712 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.763  0.084
##     V199   V200   V201   V202   V203   V204   V205   V206   V207   V208   V209
## 1  1.000  1.000  0.536  0.184  0.812  0.837  0.978  0.864 -0.630 -1.000 -1.000
## 2 -0.168 -0.314 -0.446 -1.000 -1.000 -0.999 -0.337  0.147  0.996  1.000  0.667
## 3 -0.993 -0.464  0.046  0.290  0.457  1.000  0.721 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000  0.773  0.958 -0.714 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.958 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.077  1.000  0.344 -1.000
## 6 -1.000 -1.000 -1.000 -1.000  0.073  1.000  0.265 -1.000 -1.000 -1.000 -1.000
##     V210   V211   V212   V213   V214   V215   V216   V217   V218   V219   V220
## 1 -1.000 -1.000 -0.452  0.828  1.000  1.000  1.000  1.000  1.000  1.000  1.000
## 2 -0.808  0.065  0.993  1.000  1.000  1.000  1.000  0.996  0.970  0.970  0.970
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.426  1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.545  0.989  0.432 -1.000 -1.000
## 5 -1.000  0.075  1.000  1.000  0.649  0.256 -0.200 -0.351 -0.733 -0.733 -0.733
## 6 -1.000 -1.000 -1.000  0.563  0.210 -1.000 -1.000 -0.930 -0.127  0.890  0.935
##     V221   V222 V223   V224   V225 V226   V227   V228   V229   V230   V231
## 1  1.000  0.135   -1 -1.000 -1.000   -1 -1.000 -1.000 -0.483  0.813  1.000
## 2  0.998  1.000    1  1.000  0.109   -1 -1.000 -0.830 -0.242  0.350  0.800
## 3  0.555 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.433  0.649    1  0.093 -1.000   -1 -0.959 -0.062  0.821  1.000  1.000
## 6 -0.845 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000  0.093  0.793 -0.205
##     V232   V233   V234   V235   V236   V237   V238   V239   V240  V241 V242
## 1  1.000  1.000  1.000  1.000  1.000  0.219 -0.943 -1.000 -1.000 -1.00   -1
## 2  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.616 -0.93   -1
## 3 -1.000 -1.000 -1.000  0.024  1.000  0.388 -1.000 -1.000 -1.000 -1.00   -1
## 4 -0.348  1.000  0.798 -0.935 -1.000 -1.000 -1.000 -1.000 -1.000 -1.00   -1
## 5  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.583 -0.843 -1.00   -1
## 6  0.214  0.746  0.918  0.692  0.954 -0.882 -1.000 -1.000 -1.000 -1.00   -1
##   V243 V244   V245   V246   V247   V248   V249   V250   V251   V252   V253
## 1   -1   -1 -1.000 -0.974 -0.429  0.304  0.823  1.000  0.482 -0.474 -0.991
## 2   -1   -1 -1.000 -1.000 -0.858 -0.671 -0.671 -0.033  0.761  0.762  0.126
## 3   -1   -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.109  1.000 -0.179
## 4   -1   -1 -1.000 -1.000 -1.000 -0.318  1.000  0.536 -0.987 -1.000 -1.000
## 5   -1   -1 -0.877 -0.326  0.174  0.466  0.639  1.000  1.000  0.791  0.439
## 6   -1   -1 -0.898  0.323  1.000  0.803  0.015 -0.862 -0.871 -0.437 -1.000
##     V254   V255   V256 V257
## 1 -1.000 -1.000 -1.000   -1
## 2 -0.095 -0.671 -0.828   -1
## 3 -1.000 -1.000 -1.000   -1
## 4 -1.000 -1.000 -1.000   -1
## 5 -0.199 -0.883 -1.000   -1
## 6 -1.000 -1.000 -1.000   -1
y <- taprendizaje$Numero
datos.test <- model.matrix(Numero~.,ttesting)[,-1]
modelo.lasso <- glmnet(x, y, alpha = 1, family = "multinomial") 
plot(modelo.lasso,"lambda", label=TRUE)

modelo.lasso.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
mejor.lambda <- modelo.lasso.cv$lambda.min
mejor.lambda
## [1] 0.0005928984
prediccion <- predict(modelo.lasso.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$Numero
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##         prediccion
## Actual   cero cinco cuatro dos nueve ocho seis siete tres uno
##   cero    314     1      2   1     1    1    0     0    1   0
##   cinco     2   115      0   1     1    1    2     0    5   0
##   cuatro    0     0    147   1     4    1    1     1    0   3
##   dos       1     0      3 186     0    2    1     0    3   0
##   nueve     0     0      2   1   148    0    0     5    0   0
##   ocho      4     3      0   2     0  125    0     0    6   0
##   seis      3     0      0   3     0    0  140     0    0   0
##   siete     0     2      5   1     6    1    0   153    1   0
##   tres      3     6      1   6     1    8    0     2  155   0
##   uno       0     1      1   0     1    0    1     0    1 259
## 
## $`Precisión Global`
## [1] 0.9370629
## 
## $`Error Global`
## [1] 0.06293706
## 
## $`Precisión por categoría`
##      cero     cinco    cuatro       dos     nueve      ocho      seis     siete 
## 0.9781931 0.9055118 0.9303797 0.9489796 0.9487179 0.8928571 0.9589041 0.9053254 
##      tres       uno 
## 0.8516484 0.9810606 
## 
## $`Precision Positiva`
## [1] 0.982906
## 
## $`Precision Negativa`
## [1] 0.9968254
## 
## $`Falsos Positivos`
## [1] 0.003174603
## 
## $`Falsos Negativos`
## [1] 0.01709402
## 
## $`Asertividad Positiva`
## [1] 0.9913793
## 
## $`Asertividad Negativa`
## [1] 0.9936709

###Ridge

x <- model.matrix(Numero ~ ., taprendizaje)[,-1]
head(x)
##   V2 V3 V4     V5     V6     V7     V8     V9    V10    V11    V12    V13
## 1 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -0.631  0.862 -0.167 -1.000 -1.000
## 2 -1 -1 -1 -0.813 -0.671 -0.809 -0.887 -0.671 -0.853 -1.000 -1.000 -0.774
## 3 -1 -1 -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.996  0.147  1.000
## 4 -1 -1 -1 -1.000 -1.000 -0.273  0.684  0.960  0.450 -0.067 -0.679 -1.000
## 5 -1 -1 -1 -1.000 -1.000 -0.928 -0.204  0.751  0.466  0.234 -0.809 -1.000
## 6 -1 -1 -1 -1.000 -1.000 -0.397  0.983 -0.535 -1.000 -1.000 -1.000 -1.000
##      V14    V15    V16 V17 V18 V19 V20    V21    V22    V23    V24    V25
## 1 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -0.992  0.297
## 2 -0.180  0.052 -0.241  -1  -1  -1  -1  0.392  1.000  0.857  0.727  1.000
## 3 -0.189 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.114  0.974  0.917  0.734
## 5 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -0.370  0.739  1.000  1.000
## 6 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000 -1.000  0.692  0.536 -0.767
##      V26    V27    V28    V29    V30    V31    V32 V33 V34 V35 V36    V37
## 1  1.000  0.307 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 2  0.805  0.613  0.613  0.860  1.000  1.000  0.396  -1  -1  -1  -1 -0.548
## 3 -1.000 -0.882  1.000  0.390 -0.811 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 4  0.994  1.000  0.973  0.391 -0.421 -0.976 -1.000  -1  -1  -1  -1 -0.323
## 5  1.000  1.000  0.644 -0.890 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 6 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
##      V38    V39    V40    V41    V42    V43    V44    V45 V46    V47    V48
## 1 -1.000 -1.000 -0.410  1.000  0.986 -0.565 -1.000 -1.000  -1 -1.000 -1.000
## 2  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000   1  1.000  0.875
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -0.715  1.000  0.029  -1 -1.000 -1.000
## 4  0.991  0.622 -0.738 -1.000 -0.639  0.023  0.871  1.000   1 -0.432 -1.000
## 5  0.616  1.000  0.688 -0.455 -0.731  0.659  1.000 -0.287  -1 -1.000 -1.000
## 6 -0.921  0.928 -0.118 -1.000 -1.000 -1.000 -1.000 -1.000  -1 -1.000 -1.000
##      V49 V50 V51 V52    V53    V54    V55    V56 V57    V58    V59    V60
## 1 -1.000  -1  -1  -1 -1.000 -1.000 -0.683  0.825   1  0.562 -1.000 -1.000
## 2 -0.957  -1  -1  -1 -0.786  0.961  1.000  1.000   1  0.727  0.403  0.403
## 3 -1.000  -1  -1  -1 -1.000 -0.888 -0.912 -1.000  -1 -1.000 -0.549  1.000
## 4 -1.000  -1  -1  -1  0.409  1.000  0.000 -1.000  -1 -1.000 -1.000 -0.842
## 5 -1.000  -1  -1  -1 -1.000 -0.376 -0.186 -0.874  -1 -1.000 -0.014  1.000
## 6 -1.000  -1  -1  -1 -1.000 -0.394  1.000 -0.596  -1 -1.000 -1.000 -1.000
##      V61    V62    V63   V64 V65 V66 V67    V68    V69    V70    V71    V72
## 1 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -0.938  0.540  1.000
## 2  0.171 -0.314 -0.314 -0.94  -1  -1  -1 -1.000 -0.298  1.000  1.000  1.000
## 3  0.361 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -0.938  0.694  0.057 -1.000
## 4  0.714  1.000 -0.534 -1.00  -1  -1  -1 -0.879  0.965  1.000 -0.713 -1.000
## 5 -0.253 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000 -1.000 -1.000 -1.00  -1  -1  -1 -1.000 -1.000  0.060  0.900 -0.951
##      V73    V74    V75    V76    V77    V78    V79 V80 V81 V82 V83    V84
## 1  0.778 -0.715 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 2  0.440  0.056 -0.755 -1.000 -1.000 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 3 -1.000 -1.000 -0.382  1.000  0.511 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 4 -1.000 -1.000 -1.000 -0.606  0.977  0.695 -0.906  -1  -1  -1  -1 -0.528
## 5 -1.000 -0.978  0.501  1.000 -0.540 -1.000 -1.000  -1  -1  -1  -1 -1.000
## 6 -1.000 -1.000 -1.000 -0.647  0.455 -0.333 -1.000  -1  -1  -1  -1 -1.000
##      V85    V86    V87    V88    V89    V90    V91    V92    V93    V94 V95 V96
## 1 -1.000  0.100  1.000  0.922 -0.439 -1.000 -1.000 -1.000 -1.000 -1.000  -1  -1
## 2  0.366  1.000  1.000  1.000  1.000  1.000  0.889 -0.081 -0.920 -1.000  -1  -1
## 3 -0.311  1.000 -0.043 -1.000 -1.000 -1.000 -0.648  1.000  0.644 -1.000  -1  -1
## 4  1.000  0.931 -0.888 -1.000 -1.000 -1.000 -0.949  0.559  0.984 -0.363  -1  -1
## 5 -1.000 -1.000 -0.998 -0.341  0.296  0.371  1.000  0.417 -0.989 -1.000  -1  -1
## 6 -1.000  0.259  0.676 -1.000 -1.000 -1.000 -0.984  0.677  0.981  0.551  -1  -1
##   V97 V98 V99  V100   V101   V102   V103   V104   V105  V106   V107   V108
## 1  -1  -1  -1 -1.00 -0.257  0.950  1.000 -0.162 -1.000 -1.00 -1.000 -0.987
## 2  -1  -1  -1 -1.00 -0.396  0.886  0.974  0.851  0.851  0.95  1.000  1.000
## 3  -1  -1  -1 -1.00  0.489  1.000 -0.493 -1.000 -1.000 -1.00 -0.564  1.000
## 4  -1  -1  -1 -0.97 -0.266 -0.555 -1.000 -1.000 -1.000 -1.00 -0.186  1.000
## 5  -1  -1  -1 -1.00 -1.000 -1.000 -0.008  1.000  1.000  1.00  1.000  0.761
## 6  -1  -1  -1 -1.00 -0.994  0.699  0.305 -1.000 -1.000 -1.00 -0.499  1.000
##     V109   V110 V111 V112 V113 V114 V115   V116   V117   V118   V119   V120
## 1 -0.714 -0.832   -1   -1   -1   -1   -1 -0.797  0.909  1.000  0.300 -0.961
## 2  0.539 -0.754   -1   -1   -1   -1   -1 -1.000 -1.000 -0.886 -0.505 -1.000
## 3  0.693 -1.000   -1   -1   -1   -1   -1 -0.966  0.988  1.000 -0.893 -1.000
## 4  0.488 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.731 -1.000   -1   -1   -1   -1   -1 -1.000 -1.000 -1.000  0.242  1.000
## 6 -0.092  0.751   -1   -1   -1   -1   -1 -1.000 -0.923  0.966 -0.107 -1.000
##   V121   V122   V123  V124   V125   V126   V127 V128 V129 V130 V131   V132
## 1   -1 -1.000 -0.550 0.485  0.996  0.867  0.092   -1   -1   -1   -1  0.278
## 2   -1 -0.649  0.405 1.000  1.000  0.653 -0.838   -1   -1   -1   -1 -1.000
## 3   -1 -1.000 -0.397 1.000  0.903 -0.977 -1.000   -1   -1   -1   -1 -0.559
## 4   -1 -1.000  0.697 0.992 -0.458 -1.000 -1.000   -1   -1   -1   -1 -1.000
## 5    1  0.319  0.259 1.000  0.742 -0.757 -1.000   -1   -1   -1   -1 -1.000
## 6   -1 -1.000 -0.300 0.854 -0.382  0.617 -1.000   -1   -1   -1   -1 -1.000
##     V133   V134   V135   V136   V137   V138   V139   V140   V141   V142   V143
## 1  1.000  0.877 -0.824 -1.000 -0.905  0.145  0.977  1.000  1.000  1.000  0.990
## 2 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.550  0.993  1.000  0.618
## 3  1.000  1.000 -0.297 -1.000 -1.000 -1.000 -0.611  1.000  0.873 -0.698 -0.552
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -0.341  1.000  0.608 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -0.975 -0.467 -0.989 -1.000 -1.000 -0.171  0.998  0.669 -0.945
## 6 -0.409  1.000 -0.529 -1.000 -1.000 -1.000  0.048  0.614 -0.268  0.544 -1.000
##     V144 V145  V146   V147   V148   V149   V150   V151   V152   V153   V154
## 1 -0.745   -1 -1.00 -0.950  0.847  1.000  0.327 -1.000 -1.000  0.355  1.000
## 2 -0.869   -1 -0.96 -0.512  0.134 -0.343 -0.796 -1.000 -1.000 -1.000 -1.000
## 3 -1.000   -1 -1.00 -1.000 -0.126  1.000  1.000  0.766 -0.764 -1.000 -1.000
## 4 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.471
## 5 -1.000   -1 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 6 -1.000   -1 -1.00 -1.000 -1.000  0.050  0.971 -0.839 -1.000 -1.000 -1.000
##     V155   V156   V157   V158   V159   V160 V161   V162  V163   V164   V165
## 1  0.655 -0.109 -0.185  1.000  0.988 -0.723   -1 -1.000 -0.63  1.000  1.000
## 2 -1.000 -1.000 -0.432  0.994  1.000  0.223   -1  0.426  1.00  1.000  1.000
## 3 -0.577  1.000  0.933  0.484 -0.197 -1.000   -1 -1.000 -1.00 -0.818 -0.355
## 4  0.998 -0.416 -1.000 -1.000 -1.000 -1.000   -1 -1.000 -1.00 -1.000 -1.000
## 5 -1.000 -1.000  0.228  1.000  0.038 -1.000   -1 -1.000 -1.00 -1.000 -1.000
## 6  0.172  0.526 -0.003  0.307 -1.000 -1.000   -1 -1.000 -1.00 -1.000  0.398
##     V166   V167   V168   V169   V170   V171   V172   V173   V174   V175   V176
## 1  0.068 -0.925  0.113  0.960  0.308 -0.884 -1.000 -0.075  1.000  0.641 -0.995
## 2  0.214 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.292  1.000  0.967
## 3  0.334  1.000  0.868 -0.289 -0.677 -0.596  1.000  1.000  1.000 -0.581 -1.000
## 4 -1.000 -1.000 -1.000 -0.644  0.963  0.590 -0.999 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.826  0.918  0.933 -0.794
## 6  0.459 -1.000 -1.000 -1.000 -1.000  0.372  0.555  0.520 -0.045 -1.000 -1.000
##    V177   V178   V179   V180   V181   V182   V183 V184   V185   V186   V187
## 1 -1.00 -1.000 -0.677  1.000  1.000  0.753  0.341    1  0.707 -0.942 -1.000
## 2 -0.88  0.449  1.000  0.896 -0.094 -0.750 -1.000   -1 -1.000 -1.000 -1.000
## 3 -1.00 -1.000 -1.000 -1.000 -1.000 -0.954  0.118    1  1.000  1.000  1.000
## 4 -1.00 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000   -1  0.061  1.000 -0.079
## 5 -1.00 -1.000 -1.000 -0.666  0.337  0.224 -0.908   -1 -1.000 -1.000 -1.000
## 6 -1.00 -1.000 -1.000 -1.000  0.671  0.176 -1.000   -1 -1.000 -1.000  0.236
##     V188   V189   V190   V191   V192   V193   V194   V195   V196   V197   V198
## 1 -1.000  0.545  1.000  0.027 -1.000 -1.000 -1.000 -0.903  0.792  1.000  1.000
## 2 -1.000 -1.000 -0.627  1.000  1.000  0.198 -0.105  1.000  1.000  1.000  0.639
## 3  1.000  0.973 -0.092 -0.995 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -1.000 -1.000  0.418  1.000 -0.258 -1.000 -1.000 -0.246  1.000  1.000  0.355
## 6  0.934  0.971 -0.712 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000  0.763  0.084
##     V199   V200   V201   V202   V203   V204   V205   V206   V207   V208   V209
## 1  1.000  1.000  0.536  0.184  0.812  0.837  0.978  0.864 -0.630 -1.000 -1.000
## 2 -0.168 -0.314 -0.446 -1.000 -1.000 -0.999 -0.337  0.147  0.996  1.000  0.667
## 3 -0.993 -0.464  0.046  0.290  0.457  1.000  0.721 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000  0.773  0.958 -0.714 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.958 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.077  1.000  0.344 -1.000
## 6 -1.000 -1.000 -1.000 -1.000  0.073  1.000  0.265 -1.000 -1.000 -1.000 -1.000
##     V210   V211   V212   V213   V214   V215   V216   V217   V218   V219   V220
## 1 -1.000 -1.000 -0.452  0.828  1.000  1.000  1.000  1.000  1.000  1.000  1.000
## 2 -0.808  0.065  0.993  1.000  1.000  1.000  1.000  0.996  0.970  0.970  0.970
## 3 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.426  1.000
## 4 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.545  0.989  0.432 -1.000 -1.000
## 5 -1.000  0.075  1.000  1.000  0.649  0.256 -0.200 -0.351 -0.733 -0.733 -0.733
## 6 -1.000 -1.000 -1.000  0.563  0.210 -1.000 -1.000 -0.930 -0.127  0.890  0.935
##     V221   V222 V223   V224   V225 V226   V227   V228   V229   V230   V231
## 1  1.000  0.135   -1 -1.000 -1.000   -1 -1.000 -1.000 -0.483  0.813  1.000
## 2  0.998  1.000    1  1.000  0.109   -1 -1.000 -0.830 -0.242  0.350  0.800
## 3  0.555 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 4 -1.000 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000 -1.000 -1.000 -1.000
## 5 -0.433  0.649    1  0.093 -1.000   -1 -0.959 -0.062  0.821  1.000  1.000
## 6 -0.845 -1.000   -1 -1.000 -1.000   -1 -1.000 -1.000  0.093  0.793 -0.205
##     V232   V233   V234   V235   V236   V237   V238   V239   V240  V241 V242
## 1  1.000  1.000  1.000  1.000  1.000  0.219 -0.943 -1.000 -1.000 -1.00   -1
## 2  1.000  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.616 -0.93   -1
## 3 -1.000 -1.000 -1.000  0.024  1.000  0.388 -1.000 -1.000 -1.000 -1.00   -1
## 4 -0.348  1.000  0.798 -0.935 -1.000 -1.000 -1.000 -1.000 -1.000 -1.00   -1
## 5  1.000  1.000  1.000  1.000  1.000  1.000  1.000  0.583 -0.843 -1.00   -1
## 6  0.214  0.746  0.918  0.692  0.954 -0.882 -1.000 -1.000 -1.000 -1.00   -1
##   V243 V244   V245   V246   V247   V248   V249   V250   V251   V252   V253
## 1   -1   -1 -1.000 -0.974 -0.429  0.304  0.823  1.000  0.482 -0.474 -0.991
## 2   -1   -1 -1.000 -1.000 -0.858 -0.671 -0.671 -0.033  0.761  0.762  0.126
## 3   -1   -1 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -0.109  1.000 -0.179
## 4   -1   -1 -1.000 -1.000 -1.000 -0.318  1.000  0.536 -0.987 -1.000 -1.000
## 5   -1   -1 -0.877 -0.326  0.174  0.466  0.639  1.000  1.000  0.791  0.439
## 6   -1   -1 -0.898  0.323  1.000  0.803  0.015 -0.862 -0.871 -0.437 -1.000
##     V254   V255   V256 V257
## 1 -1.000 -1.000 -1.000   -1
## 2 -0.095 -0.671 -0.828   -1
## 3 -1.000 -1.000 -1.000   -1
## 4 -1.000 -1.000 -1.000   -1
## 5 -0.199 -0.883 -1.000   -1
## 6 -1.000 -1.000 -1.000   -1
y <- taprendizaje$Numero
library(glmnet)
datos.test <- model.matrix(Numero~.,ttesting)[,-1]
modelo.ridge <- glmnet(x, y, alpha = 1, family = "multinomial") 
plot(modelo.ridge,"lambda", label=TRUE)

modelo.ridge.cv <- cv.glmnet(x, y, alpha = 1, family = "multinomial")
plot(modelo.ridge.cv)

mejor.lambda <- modelo.ridge.cv$lambda.min
mejor.lambda
## [1] 0.0005928984
prediccion <- predict(modelo.ridge.cv,datos.test,type="class",s=mejor.lambda)
Actual <- ttesting$Numero
## Matriz de Confusión
MC <- table(Actual, prediccion)
# Índices de Calidad de la predicción
indices.general(MC)
## $`Matriz de Confusión`
##         prediccion
## Actual   cero cinco cuatro dos nueve ocho seis siete tres uno
##   cero    314     1      2   1     1    1    0     0    1   0
##   cinco     2   115      0   1     1    1    2     0    5   0
##   cuatro    0     0    147   1     4    1    1     1    0   3
##   dos       1     0      3 186     0    2    1     0    3   0
##   nueve     0     0      2   1   148    0    0     5    0   0
##   ocho      4     3      0   2     0  125    0     0    6   0
##   seis      3     0      0   3     0    0  140     0    0   0
##   siete     0     2      5   1     6    1    0   153    1   0
##   tres      3     6      1   6     1    8    0     2  155   0
##   uno       0     1      1   0     1    0    1     0    1 259
## 
## $`Precisión Global`
## [1] 0.9370629
## 
## $`Error Global`
## [1] 0.06293706
## 
## $`Precisión por categoría`
##      cero     cinco    cuatro       dos     nueve      ocho      seis     siete 
## 0.9781931 0.9055118 0.9303797 0.9489796 0.9487179 0.8928571 0.9589041 0.9053254 
##      tres       uno 
## 0.8516484 0.9810606 
## 
## $`Precision Positiva`
## [1] 0.982906
## 
## $`Precision Negativa`
## [1] 0.9968254
## 
## $`Falsos Positivos`
## [1] 0.003174603
## 
## $`Falsos Negativos`
## [1] 0.01709402
## 
## $`Asertividad Positiva`
## [1] 0.9913793
## 
## $`Asertividad Negativa`
## [1] 0.9936709
  1. Compare los resultados con los obtenidos en las tareas anteriores

En este caso, los metodos de Ridge y Lasso, tienen precision global de 0.9386767 y 0.9472835, respectivamente, su precision por categoria es bastante buena es decir todos los valores en el caso de Lasso estan sobre 0,90 y en el caso de Ridge encima de 0,88. El caso de la logistica es opuesto, la estimacion no resulta buena con una precision de 0.2275417.Cabe indicar que en tareas pasadas han habido mejores resultados, una precision global en el caso de arboles aleatorios de 0.9601937 y el de XGBoosting de 0.958042, aunque el SVM radial tiene tambien precision global alta no distingue tan bien por categoria como estos metodos anteriores. Por ello resulta mejor tanto el bosques aleatorio como el XGboosting, al tener todos los numeros con precision por encima de 0,90. Con una precision global de 0.964497 el SVM radial es el que mejor estima la variabilidad del modelo. El SVM ademas de tener una precision global alta, tambien tiene una precision por categoria bastante elevada en todos los numeros aunque no tan optima como estos dos metodos anteriores, a diferencia de los otros metodos. Es decir kvecinos tambien tuvo una precision global alta pero no es preciso con todos los numeros por igual. Debe senalarse que de los modelos anteriores el de arboles de decision tambien tiene mal desempeno, igual que bayes. A pesar de ello, puede senalarse la red neuronal tanto nnet como neuralnet tardan mucho en procesar los datos, mucho mas que en el caso de los kvecinos, por lo que, para el uso de equipo con baja capacidad de procesamiento puede resultar mas optimo SVM o incluso kvencinos. En el caso de los kvecinos se obtuvo precision global de 0.9585799, aun con kvecinos es superior que con la aproximacion de redes nnet (0.8682087).